Ultra-reliable Low-latency Communications Research Articles

Fog-RAN Enabled Multi-Connectivity and Multi-Cell Scheduling Framework for Ultra-Reliable Low Latency Communication

Ultra-Reliable Low Latency Communication (URLLC) is a newly introduced service class targeting emerging Internet-of-Things (IoT) application scenarios. This paper assumes an interference-limited Fog Radio Access Network (F-RAN) setup composed of multiple Remote Radio Heads (RRHs) equipped with multiple antennas serving single-antenna users. F-RAN facilitates collaborative solutions while reducing delay by pushing the network capabilities beyond the edge. By leveraging diversity, RRHs may cooperate through silencing, reducing interference, or joint transmission strategies such as maximal ratio transmission. We derive closed-form outage probability expressions and attain their diversity gain. We validate the derived analytical results through extensive numerical simulations. Furthermore, we propose a mini-slots-based scheduling framework to serve URLLC users within their fixed latency budget. In an interference-limited regime with the proposed scheduling framework, we show that a performance gain is superior when RRHs cooperate compared to when they do not. We briefly discuss the cost of reliability, i.e., the impact on the system’s average sum throughput under cooperation. Moreover, numerical results verify that cooperating transmission schemes boost transmission reliability with a significantly improved latency performance at the cost of reduced system’s average sum throughput.

Short Analog Fountain Code With Quasi-Gray Constellation Mapping Modulation Towards uRLLC

In this paper, a capacity-approaching rateless coded modulation towards ultra-reliable low latency communications (uRLLC) is proposed, including the joint design of short block-length analog fountain code (AFC) and quasi-gray constellation mapping (QGCM) modulation at the transmitter, and a limited feedback mechanism for the receiver. First, based on the nonlinear constellation and quasi-gray mapping, we design a novel rateless coded modulation scheme called AFC-QGCM, and prove it can approach to the Shannon capacity. Further, the theoretical evaluation framework of information density between the achievable rate and signal-to-noise (SNR) of the AFC-QGCM schemes is derived under short block-length. Based on the analysis, we propose a rateless transmission scheme with a limited feedback link over the fading channel without precise channel state information (CSI) at the transmitter, which could utilize the theoretical analysis to determine the optimal coding and modulation parameters during the transmission. Both analysis and simulation results shown that the proposed scheme could approaching to the Polyanskiy-Poor-Verdu (PPV) meta converse benchmark in a wide range SNRs.

Intelligent Resource Allocations for Software-Defined Mission-Critical IoT燬ervices

Heterogeneous Internet of Things (IoT) applications generate a diversity of novelty applications and services in next-generation networks (NGN), which is essential to guarantee end-to-end (E2E) communication resources for both control plane (CP) and data plane (DP). Likewise, the heterogeneous 5th generation (5G) communication applications, including Mobile Broadband Communications (MBBC), massive Machine-Type Commutation (mMTC), and ultra-reliable low latency communications (URLLC), obligate to perform intelligent Quality-of-Service (QoS) Class Identifier (QCI), while the CP entities will be suffered from the complicated massive HIOT applications. Moreover, the existing management and orchestration (MANO) models are inappropriate for resource utilization and allocation in large-scale and complicated network environments. To cope with the issues mentioned above, this paper presents an adopted software-defined mobile edge computing (SDMEC) with a lightweight machine learning (ML) algorithm, namely support vector machine (SVM), to enable intelligent MANO for real-time and resource-constraints IoT applications which require lightweight computation models. Furthermore, the SVM algorithm plays an essential role in performing QCI classification. Moreover, the software-defined networking (SDN) controller allocates and configures priority resources according to the SVM classification outcomes. Thus, the complementary of SVM and SDMEC conducts intelligent resource MANO for massive QCI environments and meets the perspectives of mission-critical communication with resource constraint applications. Based on the E2E experimentation metrics, the proposed scheme shows remarkable outperformance in key performance indicator (KPI) QoS, including communication reliability, latency, and communication throughput over the various powerful reference methods.

Deployment of Polar Codes for Mission-Critical Machine-Type Communication Over Wireless Networks

Mission critical Machine-type Communication, also referred to as Ultra-reliable Low Latency Communication is primarily characterized by communication that provides ultra-high reliability and very low latency to concurrently transmit short commands to a massive number of connected devices. While the reduction in PHY layer overhead and improvement in channel coding techniques are pivotal in reducing latency and improving reliability, the current wireless standards dedicated to support mcMTC rely heavily on adopting the bottom layers of general-purpose wireless standards and customizing only the upper layers. The mcMTC has a significant technical impact on the design of all layers of the communication protocol stack. In this paper, an innovative bottom-up approach has been proposed for mcMTC applications through PHY layer targeted at improving the transmission reliability by implementing ultra-reliable channel coding scheme in the PHY layer of IEEE 802.11a bearing in mind short packet transmission system. To achieve this aim, we analyzed and compared the channel coding performance of convolutional codes, LDPC codes, and polar codes in wireless network on the condition of short data packet transmission. The Viterbi decoding algorithm, logarithmic belief propagation algorithm, and cyclic redundancy check - successive cancellation list decoding algorithm were adopted to CC, LDPC codes, and polar codes, respectively. Consequently, a new PHY layer for mcMTC has been proposed. The reliability of the proposed approach has been validated by simulation in terms of Bit error rate vs. SNR. The simulation results demonstrate that the reliability of IEEE 802.11a standard has been significantly improved to be at PER less 10e-5 with the implementation of polar codes. The results also show that the general-purpose wireless networks are prominent in providing short packet mcMTC with the modification needed.

Схемы модуляции для систем сотовой связи 5G/IMT-2020 и 6G

In recent years, 5G (Fifth Generation) and 6G (Sixth Generation) wireless networks have attracted extensive research interest. According to the 3GPP (3rd Generation Partnership Project) project, 5G networks should be support three main application scenarios: enhanced Mobile Broadband (eMBB), Massive Machine-Type Communications, (mMTC), Ultra-Reliable Low Latency Communication (URLLC). In addition to that, enhanced vehicle-to-everything, (eV2X) are also considered as key technology in 5G. All these scenarios require ubiquitous connectivity providing high data rates and spectral efficiency. These issues lead to many challenges for introducing 5G and 6G networks. Traditional modulation and multiple access schemes will not achieve the requirements of 5G and 6G networks. In order to meet these upcoming requirements, it is necessary to explore novel modulation and multiple access schemes. Data rate, resistance to noise and capacity of wireless network depend on choosing current modulation scheme. To meet the requirements of 5G and 6G and reduce the out-of-band (OOB) leakage various modulation schemes based on subband filtering, pulse shaping and precoding have been proposed. This article provides an overview of the different modulation schemes for 5G and 6G systems.

Admission Control and Virtual Network Embedding in 5G Networks: A Deep Reinforcement-Learning Approach

Fifth-generation (5G) networks are already available in major urban areas and are expected to bring a major transformation to citizens’ lives. 5G services, such as enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), and massive machine-type communications (mMTC), require a network infrastructure capable of supporting stringent requirements in terms of latency and bandwidth demands; as such, it must be highly dynamic and flexible. Network slicing is a key enabler technology that can provide dynamic and flexible characteristics to 5G network architecture. A network slice (NS) can be defined as a partition of network and IT resources, that is, network links and nodes capacity dedicated to a specific set of service demands. As a result, different NSs can coexist over the same physical infrastructure network and can be used to dynamically and flexibly deploy the aforementioned 5G services. However, to efficiently implement NSs with different requirements, communication service providers (CSPs) that own the physical infrastructure network must adopt sophisticated techniques for admission control and resource allocation of NSs. In this paper, we present a novel framework for admission control and resource allocation of 5G NSs in metro-core networks. Specifically, our framework is based on a deep reinforcement learning (DRL) algorithm called Advantage Actor Critic (A2C), which performs admission control, i.e. it is capable of learning which slice to admit based on the availability of the physical network resources. Then, given the diversity of requirements for each 5G service, we propose different resource allocation algorithms based on integer linear programming (ILP) and heuristics to treat each service accordingly. Results show that our proposed framework can increase the number of admitted NSs with respect to the case in which the admission control is disabled by improving the resource allocation performance.

Multi-Connectivity in 5G and Beyond Non-Terrestrial Networks

The Fifth Generation (5G) communications systems aim to serve such service classes as Ultra-Reliable Low Latency Communications (URLLC), enhanced Mobile Broadband (eMBB), and massive Machine-Type Communications (mMTC). To meet the growing requirements posed to mobile networks, satellites can be used to complement the Terrestrial Networks (TNs). To increase the efficiency of the satellite communications involved, bandwidth-efficient techniques should be used. Multi- Connectivity (MC) is one such technique. In MC, a User Equipment (UE), for example, a smartphone, can be connected to multiple Next Generation Node Bs (gNBs) simultaneously. In this paper, an adaptive MC activation scheme for throughput enhancement in 5G and beyond Non-Terrestrial Networks (NTNs) is presented. The algorithm is evaluated by system simulations using different traffic split algorithms, namely, even split, data request per connection and per gNB algorithms. In the considered simulation scenario, the maximum throughput enhancement of 9.1%, compared to when MC is turned off, is experienced when using the adaptive Secondary Node (SN) addition algorithm with the combination of the data request algorithms.

5G Networks Towards Smart and Sustainable Cities: A Review of Recent Developments, Applications and Future Perspectives

5G wireless communication systems provide massive system capacity with high data rates, very short low-latency, and ultra-high reliability, in addition to high connection density with a positive experience on smart cities and the Internet of Things (IoT). Future networks are anticipated to revolutionize typical applications such as the enhanced mobile broadband services (EMBB), ultra-reliable low latency communication (uRLLC), and massive machine-type communications (mMTC) anywhere and everywhere. This rationalizes the need to investigate the sustainable elements of 5G networks in smart cities to understand how 5G networks can be more environmentally- friendly and energy-efficient. This paper aims to investigate how 5G networks can act as key enablers in achieving sustainability in smart cities, using a macroscopic review. An overview of 5G communication networks and several 5G technologies used in smart city applications to enhance sustainability is presented. This is followed by investigating the indicators that measure sustainability in 5G networks across the environmental, social, and economic dimensions; and sub-dimensions such as energy efficiency, power consumption, carbon footprint, pollution, cost, health, safety, and security. The results show that the majority of research papers focus on the environmental dimensions of sustainability (42%) when attempting to address sustainability in 5G systems and smart cities. The findings also showed a huge interest in the economic (37%) and social (21%) dimensions as well. Further, when examining the sub-dimensions, it was found that most of the studies focused on energy efficiency (20%), power consumption (17%), and cost (15%).

CLARQ: A Dynamic ARQ Solution for Ultra-High Closed-Loop Reliability

Emerging wireless control applications demand for extremely high closed-loop reliability under strict latency constraints, which the conventional Automatic Repeat reQuest (ARQ) solutions with static schedules fail to provide. To overcome this issue and enable data-link layer error control for ultra reliable low-latency communication (URLLC) services, we propose a novel protocol: the Closed-Loop ARQ (CLARQ), which forces to accomplish an information exchange round within a fixed loop-back latency, and dynamically re-allocates the remaining resource between uplink and downlink slots upon the result of last uplink transmission. The proposed method guarantees to meet the latency requirement, while delivering high communication reliability and power efficiency. It can be efficiently offline optimized by means of dynamic programming techniques, and is capable of real-time deployment with a low-cost implementation based on look-up tables. Numerical evaluations have verified that CLARQ outperforms baselines with significantly improved closed-loop reliability and reduced energy consumption. Especially, over a Rayleigh channel with 0dB mean SNR, it is able to provide a closed-loop error rate below 1e-7 within 10ms loop-back latency, which makes our proposal competitive for practical URLLC applications in future 5G-and-beyond networks.

Performance of Multiuser Downlink Cell-Free Massive MIMO Systems With Hard Deadlines

Cell-free massive MIMO (mMIMO) is expected to be a novel network architecture to support massive connections, ultra-high transmission rate, and ultra-reliability low-latency transmission. Therefore, applications in ultra-reliable low-latency communications (URLLC) can be served by the cell-free mMIMO network in the future. This paper considers a multiuser downlink cell-free massive MIMO communication system and defines two events to evaluate the system’s failure in latency and reliability performance of the transmission. The first kind of event is defined as Transmission Error, where the value of the downlink rate becomes too high to be supported by the channel between the access points (APs) and user equipments (UEs), while the second event is defined as Time Overflow, where the time of packet delivery violates the hard deadline. The probabilities of these events can help evaluate the URLLC performance of the system because the probability of Transmission Error reflects the reliability of the transmission while the probability of Time Overflow reflects the latency of the transmission. We derive the expressions of the probabilities of these two events and the final probability system outage, with maximum ratio transmission (MRT) and zero-forcing (ZF) precoding, based on the properties of Gamma distribution and the approximation for non-isotropic vectors. We finally provide the numerical results by system-level simulations to show that the expressions are accurate and discuss the influence of the noise variance, the number of UEs, and the precoding scheme on the URLLC performance.

A Survey on 5G Radio Access Network Energy Efficiency: Massive MIMO, Lean Carrier Design, Sleep Modes, and Machine Learning

Cellular networks have changed the world we are living in, and the fifth generation (5G) of radio technology is expected to further revolutionise our everyday lives by enabling a high degree of automation, through its larger capacity, massive connectivity, and ultra-reliable low-latency communications. In addition, the third generation partnership project (3GPP) new radio (NR) specification also provides tools to significantly decrease the energy consumption and the green house emissions of next generations networks, thus contributing towards information and communication technology (ICT) sustainability targets. In this survey paper, we thoroughly review the state-of-the-art on current energy efficiency research. We first categorize and carefully analyse the different power consumption models and energy efficiency metrics, which have helped to make progress on the understanding of green wireless networks. Then, as a main contribution, we survey in detail —from a theoretical and a practical viewpoint— the main energy efficiency enabling technologies that 3GPP NR provides, together with their main benefits and challenges. Special attention is paid to four key enabling technologies, i.e., massive multiple-input multiple-output (MIMO), lean carrier design, and advanced idle modes, together with the role of artificial intelligence capabilities. We dive into their implementation and operational details, and thoroughly discuss their optimal operation points and theoretical-trade-offs from an energy consumption perspective. This will help the reader to grasp the fundamentals of —and the status on— green wireless networking. Finally, the areas of research where more effort is needed to make future wireless networks greener are also discussed.

An SDN Controller-Based Network Slicing Scheme Using Constrained Reinforcement Learning

In order to meet the strong diversification of services that demand network flexibility that will be able to serve the dire need for transmission resources, network slicing was embraced as a plausible solution. Reinforcement learning (RL) has been applied in resource allocation (RA) problems, but has not yet marked the translation from traditional optimization approaches primarily due to its inability to satisfy state constraints. The aim of this article is to address this challenge. This article proposes a logical architecture for network slicing based on software-defined networking (SDN), where an SDN controller controls the network slicing process in a centralized fashion, and manages the resource allocation (RA) process with the help of the slice manager. The considered problem jointly addresses power and channel allocation using a hybrid access mode for ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) slices. Proper assumptions on the arrival rates, packet length distributions, as well as power and delay constraints were used to design the behavior of the reward function to realize a constrained RL approach. Here, the Bellman optimality equation was reformulated into a primal-dual optimization problem through the use of Nesterov’s smoothing technique and the Legendre-Fenchel transformation. The proposed algorithm shows favorable performance over the traditional RL strategy in attributes favoring eMBB services, i.e., the average bit rate, and significantly outperforms both baselines in attributes favoring URLLC services, i.e., average latency. Systematically, on the power-delay performance evaluation, it shows that it can adapt very well in rapidly time-varying non-Markovian environments and still successfully satisfy the delay constraints of the applications hosted on a slice.

Ordered Reliability Bits Guessing Random Additive Noise Decoding

Error correction techniques traditionally focus on the co-design of restricted code-structures in tandem with code-specific decoders that are computationally efficient when decoding long codes in hardware. Modern applications are, however, driving demand for ultra-reliable low-latency communications (URLLC), rekindling interest in the performance of shorter, higher-rate error correcting codes, and raising the possibility of revisiting universal, code-agnostic decoders. To that end, here we introduce a soft-detection variant of Guessing Random Additive Noise Decoding (GRAND) called Ordered Reliability Bits GRAND that can accurately decode any moderate redundancy block-code. It is designed with efficient circuit implementation in mind, and determines accurate decodings while retaining the original hard detection GRAND algorithm's suitability for a highly parallelized implementation in hardware. ORBGRAND is shown to provide excellent soft decision block error performance for codes of distinct classes (BCH, CA-Polar and RLC) with modest complexity, while providing better block error rate performance than CA-SCL, a state of the art soft detection CA-Polar decoder. ORBGRAND offers the possibility of an accurate, energy efficient soft detection decoder suitable for delivering URLLC in a single hardware realization.

A Survey on Security Approaches on PPDR Systems Toward 5G and Beyond

The present survey seeks to contribute to the existing discussion about the changing of current Public Protection and Disaster Relief (PPDR) networks (TETRA, TETRAPOL, P25, and DMR) to commercial cellular networks (4G and 5G). The availability and robustness of these commercial networks are challenging current PPDR networks and creating opportunities for a successful transition of existing systems to commercial cellular networks. Specifically, 5G networks aim to support a massive number of heterogeneous devices while providing ultra-reliable low latency communications, higher mobility, and the capability of customised support of applications via network slicing, thus providing a natural contribution to mission critical systems. This survey details the essential mission requirements of security of PPDR systems, describes the state of the art of the most used technologies and discusses their evolution to 4G and 5G cellular networks. Furthermore, it analyses future research directions that may contribute to a successful transition of current systems to the newer cellular networks.

5G NR Configured Grant in ns-3 Network Simulator for Ultra-Reliable Low Latency Communications

5th Generation (5G) and Beyond networks are being designed to support Ultra-Reliable and Low Latency Communications (URLLC). To this end, 5G defines a new radio (NR) interface with a new mechanism at the Physical (PHY) and Medium Access Control (MAC) layers that allow reducing the latency communication. One key mechanism to reduce the latency is the scheduling scheme. Mainly, 5G defines the use of the configured grant (CG) scheduling for uplink (UL) transmissions that eliminates the need to request and assign resources for each packet transmission by pre-allocating resources to the UE. The availability of simulation tools that accurately model the new mechanisms and technologies incorporated in 5G New Radio (NR) is key to research and evaluate new proposals and enhancements to meet the communication requirements of emerging services. In this context, this paper presents the implementation of the configured grant scheduling in the ns-3 network simulator. Remarkably, the configured grant has been implemented within the 5G-LTE-EPC Network simulAtor (5G-LENA) module that simulates the fundamental PHY-MAC NR features in line with the NR specifications. In addition, this paper validates the configured grant implementation through system-level simulations considering a typical Industry 4.0 scenario characterized by applications demanding URLLC.

ЭФФЕКТИВНОСТЬ ВАРИАНТОВ МНОГОСТАНЦИОННОГО ДОСТУПА ДЛЯ СОТОВЫХ СЕТЕЙ 5G И 6G

Introduction: IMT2020 (5G) networks can significantly improve the performance of previous generation mobile communication systems in terms of improving broadband multiple access (eMBB – enhanced Mobile Broadband) and providing ultra-reliable low-latency communication (ULLRC – Ultra Low Latency Rellable Communication). The purpose of the work is to review and comparative analysis of multiple access technologies for promising mobile communication networks, which are based on OFDM. Result: The general principles of signal generation and processing when using variants of signals with OFDM using various methods of digital filtering of subcarrier frequencies are considered. For those OFDM options where a cyclic prefix may not be used (FBMC, UFMC), the possibility of increasing the information transfer rate is controversial, since the impulse responses of filters, for example, with FBMC, significantly exceed the duration of information signals, which increases intersymbol interference. Practical significance: The analysis of the general principles for constructing devices for generating and processing physical layer signals for OFDM variants with various methods of additional digital filtering of subcarrier frequencies shows that in all cases there is a significant complication of digital algorithms for generating and processing signals with a practical absence or in some cases a small gain (not more than (0.5&0.8) dB in relation to signal/noise) by the magnitude of the error per information bit. Based on the results of computer simulation, it is shown that OFDM technology and its variants do not allow obtaining breakthrough solutions in the field of physical layer signal characteristics for 5G and 6G networks, and alternative options for building group signal structures should be considered.

Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey

Fifth-generation (5G) technology will play a vital role in future wireless networks. The breakthrough 5G technology will unleash a massive Internet of Everything (IoE), where billions of connected devices, people, and processes will be simultaneously served. The services provided by 5G include several use cases enabled by the enhanced mobile broadband, massive machine-type communications, and ultra-reliable low-latency communication. Fifth-generation networks potentially merge multiple networks on a single platform, providing a landscape for seamless connectivity, particularly for high-mobility devices. With their enhanced speed, 5G networks are prone to various research challenges. In this context, we provide a comprehensive survey on 5G technologies that emphasize machine learning-based solutions to cope with existing and future challenges. First, we discuss 5G network architecture and outline the key performance indicators compared to the previous and upcoming network generations. Second, we discuss next-generation wireless networks and their characteristics, applications, and use cases for fast connectivity to billions of devices. Then, we confer physical layer services, functions, and issues that decrease the signal quality. We also present studies on 5G network technologies, 5G propelling trends, and architectures that help to achieve the goals of 5G. Moreover, we discuss signaling techniques for 5G massive multiple-input and multiple-output and beam-forming techniques to enhance data rates with efficient spectrum sharing. Further, we review security and privacy concerns in 5G and standard bodies’ actionable recommendations for policy makers. Finally, we also discuss emerging challenges and future directions.

Predictive Wireless Channel Modeling of MmWave Bands Using Machine Learning

The exploitation of higher millimeter wave (MmWave) is promising for wireless communication systems. The goals of machine learning (ML) and its subcategories of deep learning beyond 5G (B5G) is to learn from the data and make a prediction or a decision other than relying on the classical procedures to enhance the wireless design. The new wireless generation should be proactive and predictive to avoid the previous drawbacks in the existing wireless generations to meet the 5G target services pillars. One of the aspects of Ultra-Reliable Low Latency Communications (URLLC) is moving the data processing tasks to the cellular base stations. With the rapid usage of wireless communications devices, base stations are required to execute and make decisions to ensure communication reliability. In this paper, an efficient new methodology using ML is applied to assist base stations in predicting the frequency bands and the path loss based on a data-driven approach. The ML algorithms that are used and compared are Multilelayers Perceptrons (MLP) as a neural networks branch and Random Forests. Systems that consume different bands such as base stations in telecommunications with uplink and downlink transmissions and other internet of things (IoT) devices need an urgent response between devices to alter bands to maintain the requirements of the new radios (NR). Thus, ML techniques are needed to learn and assist a base station to fluctuate between different bands based on a data-driven system. Then, to testify the proposed idea, we compare the analysis with other deep learning methods. Furthermore, to validate the proposed models, we applied these techniques to different case studies to ensure the success of the proposed works. To enhance the accuracy of supervised data learning, we modified the random forests by combining an unsupervised algorithm to the learning process. Eventually, the superiority of ML towards wireless communication demonstrated great accuracy at 90.24%.

Resource allocation for a multi‐device URLLC network

AbstractThe fifth generation of wireless cellular networks, the 5G New Radio (5G NR), is expected to support ultra‐reliable low latency communications (URLLC) as one of its primary communication services. The URLLC service of the network targets very high levels of reliability, which according to the 3GPP standards, corresponds to a block error rate of to . Simultaneously, the networks need to guarantee very low latency, less than 1 millisecond according to the 3GPP standards. In this article, we propose an algorithm to solve the resource allocation problem, in terms of power and blocklength optimization, for a general multi‐device downlink URLLC in a factory automation scenario. Our objective is to minimize the decoding‐error probability at a target device, while ensuring that the rest of the devices perform under a threshold error probability. To achieve this, we propose a multi‐layered encapsulation method which simplifies the resource allocation problem of multi‐device networks. We also propose a general relay‐assisted strategy in the last step of the multi‐layered encapsulation model, which results in a remarkable improvement in the decoding‐error probability of the target device. Moreover, we also propose a multiple‐antenna beamforming design based resource allocation, which further improves the decoding‐error‐probability at the expense of higher computational complexity.

Analysis of Power Allocation for NOMA-Based D2D Communications Using GADIA

The new era of IoT brings the necessity of smart synergy for diverse communication and computation entities. The two extremes are, on the one hand, the 5G Ultra-Reliable Low-Latency Communications (URLLC) required for Industrial IoT (IIoT) and Vehicle Communications (V2V, V2I, V2X). While on the other hand, the Ultra-Low Power, Wide-Range, Low Bit-rate Communications, such as Sigfox, LoRa/LoRaWAN, NB-IoT, Cat-M1, etc.; used for smart metering, smart logistics, monitoring, alarms, tracking applications. This extreme variety and diversity must work in synergy, all inter-operating/inter-working with the Internet. The communication solutions must mutually cooperate, but there must be a synergy in a broader sense that includes the various communication solutions and all the processing and storage capabilities from the edge cloud to the deep-cloud. In this paper, we consider a non-orthogonal multiple access (NOMA)-based device to device (D2D) communication system coexisting with a cellular network and utilize Greedy Asynchronous Distributed Interference Avoidance Algorithm (GADIA) for dynamic frequency allocation strategy. We analyze a max–min fairness optimization problem with energy budget constraints to provide a reasonable boundary rate for the downlink to all devices and cellular users in the network for a given total transmit power. A comprehensive simulation and numerical evaluation is performed. Further, we compare the performance of maximum achievable rate and energy efficiency (EE) at a given spectral efficiency (SE) while employing NOMA and orthogonal frequency-division multiple access (OFDMA).