Machine Type Communication Research Articles

Optimizing Open Radio Access Network Systems with LLAMA V2 for Enhanced Mobile Broadband, Ultra-Reliable Low-Latency Communications, and Massive Machine-Type Communications: A Framework for Efficient Network Slicing and Real-Time Resource Allocation.

This study presents an advanced framework integrating LLAMA_V2, a large language model, into Open Radio Access Network (O-RAN) systems. The focus is on efficient network slicing for various services. Sensors in IoT devices generate continuous data streams, enabling resource allocation through O-RAN's dynamic slicing and LLAMA_V2's optimization. LLAMA_V2 was selected for its superior ability to capture complex network dynamics, surpassing traditional AI/ML models. The proposed method combines sophisticated mathematical models with optimization and interfacing techniques to address challenges in resource allocation and slicing. LLAMA_V2 enhances decision making by offering explanations for policy decisions within the O-RAN framework and forecasting future network conditions using a lightweight LSTM model. It outperforms baseline models in key metrics such as latency reduction, throughput improvement, and packet loss mitigation, making it a significant solution for 5G network applications in advanced industries.

Alignment of Heterogeneous Packet Lengths for Random Access in 5G Massive Machine Type Communication

Alignment of Heterogeneous Packet Lengths for Random Access in 5G Massive Machine Type Communication

Multi-Layered Unsupervised Learning Driven by Signal-to-Noise Ratio-Based Relaying for Vehicular Ad Hoc Network-Supported Intelligent Transport System in eHealth Monitoring.

Every year, about 1.19 million people are killed in traffic accidents; hence, the United Nations has a goal of halving the number of road traffic deaths and injuries by 2030. In line with this objective, technological innovations in telecommunication, particularly brought about by the rise of 5G networks, have contributed to the development of modern Vehicle-to-Everything (V2X) systems for communication. A New Radio V2X (NR-V2X) was introduced in the latest Third Generation Partnership Project (3GPP) releases which allows user devices to exchange information without relying on roadside infrastructures. This, together with Massive Machine Type Communication (mMTC) and Ultra-Reliable Low Latency Communication (URLLC), has led to the significantly increased reliability, coverage, and efficiency of vehicular communication networks. The use of artificial intelligence (AI), especially K-means clustering, has been very promising in terms of supporting efficient data exchange in vehicular ad hoc networks (VANETs). K-means is an unsupervised machine learning (ML) technique that groups vehicles located near each other geographically so that they can communicate with one another directly within these clusters while also allowing for inter-cluster communication via cluster heads. This paper proposes a multi-layered VANET-enabled Intelligent Transportation System (ITS) framework powered by unsupervised learning to optimize communication efficiency, scalability, and reliability. By leveraging AI in VANET solutions, the proposed framework aims to address road safety challenges and contribute to global efforts to meet the United Nations' 2030 target. Additionally, this framework's robust communication and data processing capabilities can be extended to eHealth monitoring systems, enabling real-time health data transmission and processing for continuous patient monitoring and timely medical interventions. This paper's contributions include exploring AI-driven approaches for enhanced data interaction, improved safety in VANET-based ITS environments, and potential applications in eHealth monitoring.

An Improved Phase Noise Reduction in Millimeter-Wave FBMC Systems

The next generation mobile network needs to have a spectrum above the 30 GHz radio band. The 5G system is a promising technology for future communication systems. It has been used in Device-to-Device (D2D) communications, IoT, Machine type communications (MTC), and wireless backhaul. The 5G is expected to have speeds of hundreds of times and satisfy the requirements of larger traffic density, low latency, connective density, higher capacity & reliability, data rate, super high mobility, etc. It is expected to have applications such as HD video, virtual reality & augmented reality, online games, and cloud desktops. For this, Millimeter wave (Mmwave) has been used for fifth-generation mobile networks. The millimeter wave has a frequency range from 30 to 300 GHz. When the usage of frequency is above 60 GHz, the band will acquire RF impairments such as PAPR, Phase noise, non-linearity, and phase and quadrature timing mismatch (IQTM). This article shows the Phase noise reduction in MIMO FBMC systems using Extended Kalman filter techniques.

Dynamic priority-based bandwidth allocation scheme for machine type communications

The burgeoning realm of Machine Type Communications (MTC) has ushered in a paradigm shift in wireless communication, necessitating innovative strategies to efficiently manage diverse application requirements. This paper presents a pioneering Dynamic Priority-based Bandwidth Allocation (DPBA) scheme tailored specifically for MTC scenarios, encompassing both MTC and Human Type Communications (HTC) coexistence. DPBA employs an adaptive framework addressing dynamic MTC traffic, optimizing resource use while meeting latency constraints and sporadic data patterns. By dynamically prioritizing MTC applications based on requirements, the scheme allocates bandwidth for reliable data delivery. DPBA extends to MTC and HTC coexistence, managing diverse quality demands. This underlines its versatility and capacity to serve distinct needs in a shared spectrum. To substantiate its performance, the DPBA scheme is rigorously evaluated against a spectrum of related allocation methods like Proportional Fairness (PF), Static Priority Scheduling (SPS), and the Machine Learning-based Scheme (MLS), DPBA excels in simulations, minimizing packet loss, latency, enhancing throughput and ensuring fairness, surpassing benchmarks. This research underscores the critical importance of tailored bandwidth allocation strategies in advancing MTC and HTC coexistent environments. With its adaptability, efficiency, and remarkable performance, the DPBA scheme emerges as a cornerstone in the trajectory of wireless communication networks, catering to the distinct requirements of both MTC and HTC applications.

Deep Learning Aided Intelligent Reflective Surfaces for 6G: A Survey

The envisioned sixth-generation (6G) networks anticipate robust support for diverse applications, including massive machine-type communications, ultra-reliable low-latency communications, and enhanced mobile broadband. Intelligent Reflecting Surfaces (IRS) have emerged as a key technology capable of intelligently reconfiguring wireless propagation environments, thereby enhancing overall network performance. Traditional optimization techniques face limitations in meeting the stringent performance requirements of 6G networks due to the intricate and dynamic nature of the wireless environment. Consequently, Deep Learning (DL) techniques are employed within the IRS framework to optimize wireless system performance. This paper provides a comprehensive survey of the latest research in DL-aided IRS models, covering optimal beamforming, resource allocation control, channel estimation and prediction, signal detection, and system deployment. The focus is on presenting promising solutions within the constraints of different hardware configurations. The survey explores challenges, opportunities, and open research issues in DL-aided IRS, considering emerging technologies such as digital twins (DTs), computer vision (CV), blockchain, network function virtualization (NFC), integrated sensing and communication (ISAC), software-defined networking (SDN), mobile edge computing (MEC), unmanned aerial vehicles (UAVs), and non-orthogonal multiple access (NOMA). Practical design issues associated with these enabling technologies are also discussed, providing valuable insights into the current state and future directions of this evolving field.

Combining 5G New Radio, Wi-Fi, and LiFi for Industry 4.0: Performance Evaluation.

Fifth-generation mobile networks (5G) are designed to support enhanced Mobile Broadband, Ultra-Reliable Low-Latency Communications, and massive Machine-Type Communications. To meet these diverse needs, 5G uses technologies like network softwarization, network slicing, and artificial intelligence. Multi-connectivity is crucial for boosting mobile device performance by using different Wireless Access Technologies (WATs) simultaneously, enhancing throughput, reducing latency, and improving reliability. This paper presents a multi-connectivity testbed from the 5G-CLARITY project for performance evaluation. MultiPath TCP (MPTCP) was employed to enable mobile devices to send data through various WATs simultaneously. A new MPTCP scheduler was developed, allowing operators to better control traffic distribution across different technologies and maximize aggregated throughput. Our proposal mitigates the impact of limitations on one path affecting others, avoiding the Head-of-Line blocking problem. Performance was tested with real equipment using 5GNR, Wi-Fi, and LiFi -complementary WATs in the 5G-CLARITY project-in both static and dynamic scenarios. The results demonstrate that the proposed scheduler can manage the traffic distribution across different WATs and achieve the combined capacities of these technologies, approximately 1.4 Gbps in our tests, outperforming the other MPTCP schedulers. Recovery times after interruptions, such as coverage loss in one technology, were also measured, with values ranging from 400 to 500 ms.

Optimization of resource allocation in 5G networks: A network slicing approach with hybrid NOMA for enhanced uRLLC and eMBB coexistence

SummaryTraditional Orthogonal Multiple Access (OMA) and spectrum sharing methods struggle to provide the diverse quality of service (QoS) demands for enhanced mobile broadband (eMBB), ultra‐reliable low latency communications (uRLLC), and massive machine type communications (mMTC) leading to suboptimal performance and service quality degradation. Single‐carrier‐non‐orthogonal multiple access (SC‐NOMA) appears to be a more optimized solution. It can serve multiple users simultaneously on the same time‐frequency resources. This approach offers both enhanced spectrum efficiency and meets the QoS requirements for the coexistence of eMBB, uRLLC, and mMTC. However, SC‐NOMA has some drawbacks. Decoding a user's signal involves a complex successive interference cancellation (SIC) process that gets harder with more users causing delays and errors. Additionally, strong user signals can interfere with weaker ones, limiting the number of users per channel. In order to overcome the drawbacks associated with OMA and SC‐NOMA, this paper introduces a new method called user‐paired NOMA (hybrid NOMA). Hybrid NOMA adopts a strategic approach, employing two user pairing techniques: near‐far/far‐near (NF‐FN) and near‐near/far‐far (NN‐FF). NF‐FN pairing prioritizes users with similar signal strengths but different distances from the base station. This minimizes interference for the weaker user during SIC. NN‐FF pairing, on the other hand, groups users with similar signal strengths and proximity. This approach further simplifies SIC and minimizes potential interference altogether. The simulation results demonstrate trade‐offs between eMBB and uRLLC performance. OMA suffers with dedicated resource allocation, while SC‐NOMA balances performance but experiences interference. NN‐FF prioritizes eMBB and offers best latency, while NF‐FN prioritizes uRLLC with high spectral efficiency but suffers from higher latency. Finally, by providing a thorough grasp of how hybrid NOMA resource allocation works to improve the performance of various use cases, this research makes a significant contribution to the field of 5G spectrum optimization.

Challenges and Opportunities of 5G Network: A Review of Research and Development

The advent of the fifth generation of wireless technology, commonly referred to as 5G, heralds a transformative era in communication and technology interaction. With its promise of high-speed connectivity, minimal latency, and support for massive machine-type communications, 5G networks are poised to enable a wide array of applications across various domains such as the Internet of Things (IoT), smart cities, virtual and augmented reality, Industry 4.0, and edge computing. However, the deployment of 5G networks presents numerous challenges, including infrastructure requirements, limited availability of high-frequency spectrum, security vulnerabilities, high deployment costs, and interoperability issues with existing networks. This paper meticulously examines the challenges, opportunities, and applications of 5G networks. It delves into the potential of 5G networks to offer rapid connectivity, facilitate novel applications, and revolutionize industries. Moreover, the paper scrutinizes the hurdles associated with deploying and sustaining 5G networks, underscoring the imperative need for further research in this domain to ensure the seamless adoption and integration of 5G technology. As 5G networks continue to evolve, addressing these challenges will be crucial for unlocking the full potential of this transformative technology and realizing its societal and economic benefits on a global scale. Additionally, collaboration between stakeholders including governments, telecommunications companies, technology providers, and regulatory bodies will play a pivotal role in overcoming these challenges and driving the successful deployment and adoption of 5G networks worldwide.

The 5G Era: A Comprehensive Review of Recent Advancements and Applications

The fifth generation is a promising and transformative leap in network capabilities, bringing out applications beyond traditional technologies. This review paper discusses the novel technologies shaping 5G, studying their impact and potential. We explore technological advancements like Network Function Virtualization (NFV) and Software- Defined Networking (SDN) for their role in creating flexible and efficient networks. Services like Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (uRLLC), and Massive Machine-Type Communication (mMTC) are also examined, along with their details, and role in shaping the benefits of 5G for various fields. This paper also investigates how 5G addresses the aspects like energy efficiency enhancements and network security enhancements brought about in 5G. We discuss the details of integration of Artificial Intelligence (AI) for network optimization, security, automation and user experience, along with its impact in various domains like healthcare, manufacturing, education and transportation. Further, the paper explores the incorporation of edge computing and its role in supporting real-time applications. Finally, we examine the growing importance of Non-Public Networks (NPNs) tailored for private use cases, another aspect that adds to the benefits and novelty of the fifth generation. This review provides a comprehensive overview of the key technological advancements driving the 5G revolution, highlighting their role in transforming communication infrastructures and other key use cases, supporting a new era of communication.

Q-Learning Based Load Balancing in Heterogeneous Networks with Human and Machine Type Communication Co-existence

Q-Learning Based Load Balancing in Heterogeneous Networks with Human and Machine Type Communication Co-existence

Grant-Free Random Access Enhanced by Massive MIMO and Non-Orthogonal Preambles

Massive multiple input multiple output (MIMO) enabled grant-free random access (mGFRA) stands out as a promising random access (RA) solution, thus effectively addressing the need for massive access in massive machine-type communications (mMTCs) while ensuring high spectral efficiency and minimizing signaling overhead. However, the bottleneck of mGFRA is mainly dominated by the orthogonal preamble collisions, since the orthogonal preamble pool is small and of a fixed-sized. In this paper, we explore the potential of non-orthogonal preambles to overcome limitations and enhance the success probability of mGFRA without extending the length of the preamble. Given the RA procedure of mGFRA, we analyze the factors influencing the success rate of mGFRA with non-orthogonal preamble and propose to use two types of sequences, namely Gold sequence and Gaussian distribution sequence, as the preambles for mGFRA. Simulation results demonstrate the effectiveness of these two types pf non-orthogonal preambles in improving the success probability of mGFRA. Moreover, the system parameters’ impact on the performance of mGFRA with non-orthogonal preambles is examined and deliberated.

Joint Activity Detection and Channel Estimation for Clustered Massive Machine Type Communications

Joint Activity Detection and Channel Estimation for Clustered Massive Machine Type Communications

Advanced channel coding schemes for B5G/6G networks: State‐of‐the‐art analysis, research challenges and future directions

SummaryAs a consequence of continuing demand for additional capacity and higher quality data transmission, channel coding as a key component of a digital communication network has progressed from a classical single pair, point‐to‐point information theoretic application to a class of coding techniques with diverse parameters. The heterogeneous requirements of B5G/6G networks technology have made flexibility of code parameters the main desired feature while designing channel codes. Owing to this, channel coding techniques have evolved to support enabling applications that depend on different factors such as latency, reliability, energy efficiency and throughput. We review and discuss the application of new techniques, modifications in the design and construction of modern channel coding schemes, including block, convolutional, rateless and space‐time codes. Further, the error correction performance of mainstream channel decoders for LDPC, polar and turbo codes is compared and analysed for their attainable error rate. The aim of this study is to highlight the adaptability of the discussed coding schemes for the forthcoming cellular network landscape. This article also addresses the implementational challenges of high throughput, low latency and machine type communication scenarios. Moreover, some recent studies exploring the role of machine learning for optimisation of B5G/6G networks are classified and discussed. Concluding, research areas pertaining to the scalability of error control coding for next generation networking are addressed.

A Learning-Based Energy-Efficient Device Grouping Mechanism for Massive Machine-Type Communication in the Context of Beyond 5G Networks

With the increasing demand for high data rates, low delay, and extended battery life, managing massive machine-type communication (mMTC) in the beyond 5G (B5G) context is challenging. MMTC devices, which play a role in developing the Internet of Things (IoT) and smart cities, need to transmit short amounts of data periodically within a specific time frame. Although blockchain technology is utilized for secure data storage and transfer while digital twin technology provides real-time monitoring and management of the devices, issues such as constrained time delays and network congestion persist. Without a proper data transmission strategy, most devices would fail to transmit in time, thus defying their relevance and purpose. This work investigates the problem of massive random access channel (RACH) attempts while emphasizing the energy efficiency and access latency for mMTC devices with critical missions in B5G networks. Using machine learning techniques, we propose an attention-based reinforcement learning model that orchestrates the device grouping strategy to optimize device placement. Thus, the model guarantees a higher probability of success for the devices during data transmission access, eventually leading to more efficient energy consumption. Through thorough quantitative simulations, we demonstrate that the proposed learning-based approach significantly outperforms the other baseline grouping methods.

Proportional-fair uplink resource allocation with statistical QoS provisioning for RAN slicing

This paper investigates the uplink resource allocation issue for radio access network (RAN) slicing with a focus on three primary fifth generation (5G) use cases, namely ultra-reliable low-latency communications (uRLLC), enhanced mobile broadband (eMBB) and massive machine-type communications (mMTC). Most of the existing resource allocation schemes for RAN slicing were designed to address the use cases in downlink communications. However, these schemes are not applicable to the uplink RAN slicing, even though the primary use cases also entail stringent requirements in the uplink. To this end, we aim to develop a new, fairness-aware uplink resource allocation scheme for RAN slicing, catering to the three 5G use cases. Firstly, we formulate a new uplink resource allocation optimization problem that maximizes a sum logarithmic utility for ensuring proportional-fair effective capacity distribution and resource allocation among RAN slices, subject to the distinct quality of service (QoS) requirements of the uRLLC, eMBB and mMTC slices. Then, an efficient, hierarchical resource allocation framework is then proposed to solve the problem deterministically, whereby resource block assignment is first performed at the base station via a demand-oriented greedy algorithm, followed by power allocation using a bisection method at individual user equipment. Results show that the proposed scheme outperforms the baseline schemes with fairness and QoS provisioning performance gains of up to 44.3% and 19.17% respectively.

Learning-Based Autonomous Channel Access in the Presence of Hidden Terminals

We consider the problem of autonomous channel access (AutoCA), where a group of terminals tries to discover a communication strategy with an access point (AP) via a common wireless channel in a distributed fashion. Due to the irregular topology and the limited communication range of terminals, a practical challenge for AutoCA is the hidden terminal problem, which is notorious in wireless networks for deteriorating throughput and delay performances. To meet the challenge, this paper presents a new multi-agent deep reinforcement learning paradigm, dubbed MADRL-HT, tailored for AutoCA in the presence of hidden terminals. MADRL-HT exploits topological insights and transforms the observation space of each terminal into a scalable form independent of the number of terminals. To compensate for the partial observability, we put forth a look-back mechanism such that the terminals can infer behaviors of their hidden terminals from the carrier-sensed channel states as well as feedback from the AP. A window-based global reward function is proposed, whereby the terminals are instructed to maximize the system throughput while balancing the terminals' transmission opportunities over the course of learning. Considering short-packet machine-type communications, extensive numerical experiments verified the superior performance of our solution benchmarked against the legacy carrier-sense multiple access with collision avoidance (CSMA/CA) protocol.

Enhancing Efficiency and Security in MTC Environments: A Novel Strategy for Dynamic Grouping and Streamlined Management

This study presents a new strategy to improve security and efficiency in Machine-Type Communication (MTC) networks, addressing the drawbacks of the existing Adaptive Hierarchical Group-based Mutual Authentication and Key Agreement (AHGMAKA) protocol. The AHGMAKA protocol, crucial for securing communication within groups of devices with limited resources, has been found to cause significant operational delays and inefficiencies. Our proposed solution integrates advanced cryptographic methods, including an optimized Authenticated Message Authentication Code (AMAC) and lightweight encryption, sophisticated optimization algorithms for dynamic grouping, and an efficient, lightweight group management protocol. It also introduces adaptive network management strategies to customize performance according to the needs of MTC networks. The effectiveness of this approach has been validated through empirical analysis, showing considerable improvements in operational performance and energy efficiency. These improvements mark a significant step toward achieving an optimal balance between efficiency and security for MTC networks. However, the research acknowledges ongoing challenges, including the trade-off between security and efficiency and the issue of compatibility with older devices, suggesting these as areas for future study. The paper outlines potential research paths, including using machine learning for better group management, adopting post-quantum cryptographic methods, applying hardware acceleration, and pushing to standardize these technologies. This work significantly advances the field of secure and efficient communication in MTC, a critical component of the growing Internet of Things (IoT) landscape, setting the stage for future breakthroughs.

DDPG 및 연합학습 기반 5G 네트워크 자원 할당과 트래픽 예측

With the advent of 5G, characterized by Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC), efficient network management and service provision are becoming increasingly critical. This paper proposes a novel approach to address key challenges of 5G networks, namely ultra-high speed, ultra-low latency, and ultra-reliability, while dynamically optimizing network slicing and resource allocation using machine learning (ML) and deep learning (DL) techniques. The proposed methodology utilizes prediction models for network traffic and resource allocation, and employs Federated Learning (FL) techniques to simultaneously optimize network bandwidth, latency, and enhance privacy and security. Specifically, this paper extensively covers the implementation methods of various algorithms and models such as Random Forest and LSTM, thereby presenting methodologies for the automation and intelligence of 5G network operations. Finally, the performance enhancement effects achievable by applying ML and DL to 5G networks are validated through performance evaluation and analysis, and solutions for network slicing and resource management optimization are proposed for various industrial applications.

A Multi-Agent Reinforcement Learning-Based Grant-Free Random Access Protocol for mMTC Massive MIMO Networks

The expected huge number of connected devices in Internet of Things (IoT) applications characterizes the massive machine-type communication (mMTC) scenario, one prominent use case of beyond fifth-generation (B5G) systems. To meet mMTC connectivity requirements, grant-free (GF) random access (RA) protocols are seen as a promising solution due to the small amount of data that MTC devices usually transmit. In this paper, we propose a GF RA protocol based on a multi-agent reinforcement learning approach, applied to aid IoT devices in selecting the least congested RA pilots. The rewards obtained by the devices in collision cases resemble the congestion level of the chosen pilot. To enable the operation of the proposed method in a realistic B5G network scenario and aiming to reduce signaling overheads and centralized processing, the rewards in our proposed method are computed by the devices taking advantage of a large number of base station antennas. Numerical results demonstrate the superior performance of the proposed method in terms of latency, network throughput, and per-device throughput compared with other protocols.