Mohammad Aftab Alam Khan*
| Hazilah Mad Kaidi
© 2023 IIETA. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
OPEN ACCESS
The advent of next-generation wireless networks and the Internet of Things (IoT) has introduced numerous challenges in terms of quality of service (QoS), user data rates, throughput, and security. These challenges necessitate innovative solutions to optimize performance and ensure robust security. Machine Learning (ML) has emerged as an influential tool in this regard, offering the potential to fully harness the capabilities of next-generation wireless networks and the IoT. With an ever-increasing number of connected devices and the commensurate data proliferation, ML presents an effective means of analyzing and processing this data. One significant challenge addressed by ML is network optimization. Through the analysis of network traffic patterns, congestion points are identified, and potential network performance issues are predicted. Security, a critical concern in next-generation wireless networks and the IoT, is another facet where ML proves instrumental by detecting and mitigating security breaches. This is achieved by analyzing data to identify anomalous behaviour and potential threats. Moreover, ML facilitates informed decision-making in IoT systems. By scrutinizing real-time data generated by IoT devices, ML algorithms reveal valuable insights, trends, and correlations. This capability enables IoT-enabled systems to make data-driven decisions, thus enhancing the efficiency of various applications such as smart cities, industrial automation, healthcare, and environmental monitoring. This study undertakes a systematic review of the impact of ML techniques, such as reinforcement learning, deep learning, transfer learning, and federated learning, on next-generation wireless networks, placing a particular emphasis on the IoT. The literature is reviewed systematically and studies are categorized based on their implications. The aim is to highlight potential challenges and opportunities, providing a roadmap for researchers and scholars to explore new approaches, overcome challenges, and leverage potential opportunities in the future.
Internet of Things (IoT), Machine Learning (ML), quality of service, deep learning, reinforcement learning, 5G and beyond, next-generation wireless networks
The landscape of wireless communication systems has undergone substantial evolution over the past decades, primarily fuelled by technological advancements and the escalating demand for mobile computing and pervasive connectivity. The genesis of this evolution can be traced back to the establishment of commercial mobile telephony in the 1980s, followed by the widespread adoption of Wi-Fi in the 1990s, and subsequently, the expansion of mobile broadband and the Internet of Things (IoT) in the 21st century [1, 2]. The present study embarks on a comprehensive and systematic review of Machine Learning-based methodologies applied in various facets of next-generation telecommunication networks, with a particular emphasis on the IoT paradigm. The amalgamation of IoT and next-generation networks unveils a multitude of potential arenas where Machine Learning (ML) can make significant contributions. For instance, the spatial and temporal big data that IoT continuously generates requires transmission over the network for processing, analysis, and inference by edge, fog, and cloud computing servers. This scenario presents a myriad of research gaps and challenges that call for effective solutions from the ML research community.
The most recent milestone in the evolution of wireless communication is the advent of fifth-generation (5G) technology, which stands poised to usher in a new era of connectivity and performance. By enabling previously impossible applications and services, 5G networks promise higher data rates, lower latency, and enhanced reliability compared to their predecessors. Furthermore, 5G networks have been engineered to cater to the growing connectivity demands of IoT devices, which are anticipated to proliferate exponentially due to the introduction of smart and wearable devices, wireless sensor networks (WSNs), and mobile ad-hoc networks (MANETs). As such, the evolution of wireless communication has revolutionized our lifestyle and work culture, providing constant connectivity and real-time access to information [3].
The IoT, constituting a network of interconnected physical devices, vehicles, buildings, and other entities embedded with sensors, software, and network connectivity, facilitates data collection and exchange. The role of IoT devices in wireless communication systems is pivotal in providing a seamless communication experience for users [4]. Given the exponential growth in the number of connected devices and the escalating demand for higher data rates, IoT-enabled wireless communication systems are becoming indispensable to contemporary society.
These systems are expected to provide reliable and secure communication links between IoT devices and other communication networks, thereby facilitating the delivery of innovative services and applications. The integration of IoT devices with wireless communication systems is anticipated to stimulate the growth of smart cities, the industrial Internet, and other IoT-based applications, thus paving the path towards a more connected and intelligent future [5-7].
IoT devices are designed to generate vast volumes of data from diverse sensors continuously. However, the local computational resources required to process this data are typically lacking in IoT networks. As a result, the data is transmitted via robust communication links to edge, fog, or cloud servers for further processing. It is in this context that Machine Learning (ML) plays a crucial role, helping to discern hidden patterns and trends in this big data. The insights derived from ML algorithms are subsequently used to inform decision support and expert systems.
The remainder of this paper is structured as follows: Section 2 introduces the taxonomy of the literature review, followed by a comprehensive review of the literature in Section 3. Section 4 presents challenges and opportunities for ML in IoT and next-generation wireless communication systems. Finally, Section 5 provides the conclusion.
The architecture of this systematic literature review is depicted in a hierarchical flow that is further classified by the taxonomy presented in this section. The review commences with an exploration of the evolution of wireless communication systems, detailing the progression of their generations, the data rates supported, and the temporal developments. This forms the base of the review's pyramid structure.
In the subsequent generations of wireless communication systems, particularly in 5G and beyond, the integration of IoT forms the second tier of the pyramid. This incorporation marks a significant milestone in the evolution of wireless communication systems.
The final tier of the pyramid comprises the examination of the implications and applications of various Machine Learning approaches within the IoT and next-generation wireless communication systems. This phase of the literature review is pivotal in understanding the role and impact of Machine Learning in these advanced systems.
Following this systematic review, a compilation of challenges and opportunities is presented, based on the insights gleaned from the reviewed literature. Concluding remarks are then provided, summarizing the key findings and implications of the review. The schematic representation of this literature review is illustrated in Figure 1.
Table 1 elaborates on the taxonomy, focusing on the critical domains within next-generation wireless networks where ML has been deployed. These areas include adaptive communication, Non-Orthogonal Multiple Access (NOMA), and radio resource allocation. The subsequent sections present a series of thematic tables, where relevant studies are catalogued and discussed in relation to their respective topics. This structured approach to the literature review ensures a comprehensive understanding of the role of Machine Learning in next-generation wireless communication systems and IoT.
Figure 1. Flow of literature review
Table 1. Topic-wise taxonomy mapping
|
Table ID |
Topics |
Articles |
|
2 |
ML in resource management |
[8-13] |
|
3 |
ML in adaptive communication |
[14-19] |
|
4 |
ML in NOMA systems |
[20-26] |
This section presents a systematic review of the literature on Machine Learning implications in IoT and next-generation wireless communication systems.
3.1 Evolution in wireless communication
Wireless communication has evolved significantly over the past few decades and has played a critical role in shaping the modern world as we know it today. The evolution of wireless communication can be divided into several generations, each characterized by advances in technology and the development of new standards.
The evolution of wireless communication has been driven by the increasing demand for mobile connectivity and the need to support new and more demanding applications. As technology continues to evolve, future generations of wireless communication will likely bring even more advanced capabilities, enabling new and innovative applications and transforming the way we live and work.
3.2 Mobile computing, IoT and wireless communication
The IoT refers to the interconnected network of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, and network connectivity, allowing these objects to collect and exchange data. IoT devices can communicate and collaborate with the surrounding environment, enabling them to collect and analyze data, make decisions, and perform tasks without human intervention. The IoT has a wide range of applications across various industries and sectors, some of which are:
These are just a few examples of the many areas where IoT is being used to improve efficiency, reduce costs, and create new and innovative solutions. As technology continues to evolve, IoT will likely have an even greater impact on our daily lives and the way we work and interact with the world around us. All the said technological advances require and set the room of 5G and beyond the network. That is mainly due to the inherent need for connectivity, speed, data rates and low latency with support to excessively increase the number of connected devices.
The wireless sensor network (WSN) is a specific type of IoT technology that uses wireless communication to connect many low-power, small sensors, and devices. The WSNs are designed for applications in which many small sensors are deployed to collect data and send it to a central location for processing and analysis. WSNs can be used in a wide range of applications, including environmental monitoring, industrial process control, healthcare, military, and many others [4, 29]. The WSNs are a crucial part of the IoT ecosystem and are used in many IoT applications.
The WSNs have several advantages over traditional wired sensor networks. They are easy to install and maintain, as there are no wires to run or cables to connect. They can be deployed in remote or hard-to-reach locations, and they can be reconfigured on-the-fly to adapt to changing conditions. WSNs are also energy-efficient, as they are designed to use low power, allowing the sensors to run for long periods on batteries. However, WSNs also have some challenges, including limited bandwidth, interference, security, and scalability. Researchers and engineers are constantly working to address these challenges and improve the performance of WSNs. As technology continues to evolve, WSNs are becoming increasingly popular and widely used for a variety of applications [4, 30].
Wearables are also a specific type of IoT device that can be worn on the body and are designed to be used near the user. Examples of wearables include smartwatches, fitness trackers, and smart glasses. Wearables are equipped with sensors, computing power, and wireless connectivity, which enables them to collect and exchange data with other devices. Wearables are a growing part of the IoT ecosystem and are used in many applications, including health and fitness tracking, mobile payments, and hands-free control of other devices. They allow users to access and control information and perform tasks without having to physically interact with a device. Mobile computing technologies play a crucial role in enabling the IoT and its various applications. That is under such communication systems the said technologies can be affordable at a user level. Some of the mobile computing technologies used in IoT include, but are not limited to:
The impact of mobile computing technologies on wireless communication systems has been significant in recent years. The integration of mobile computing technologies into wireless communication systems has led to the creation of smart and connected devices that can communicate with each other, resulting in new opportunities for innovation and growth in various industries [31, 32]. The rise of the IoT has been a key driver for this development, as more and more devices are being connected to the Internet to collect, process, and exchange data. This has created the need for wireless communication systems that can support many devices, have low power consumption, and low latency [33, 34]. To meet these requirements, new mobile computing technologies, such as 5G, have been developed to improve wireless communication capabilities and support the growth of IoT applications. The combination of IoT and mobile computing technologies has enabled the development of new use cases, such as smart homes, smart cities, and connected vehicles, among others [35]. Overall, the impact of mobile computing technologies on wireless communication systems has been transformative, providing new opportunities and driving innovation in a variety of industries.
The integration of IoT devices, wireless communication systems, and mobile computing technologies is expected to play a major role in enabling the next generation of smart and connected applications [36]. IoT devices can collect and exchange data with each other and with other communication networks through wireless communication systems. These systems are designed to provide reliable and secure communication links between IoT devices, enabling the delivery of innovative services and applications. Mobile computing technologies, on the other hand, provide the computational power and storage needed to process and analyze the vast amounts of data generated by IoT devices. The combination of IoT devices, wireless communication systems, and mobile computing technologies is expected to drive the growth of various applications, including smart cities, industrial Internet, and wearable technologies. These applications require low-latency, high-bandwidth, and reliable communication links, which can only be provided by the integration of IoT devices, wireless communication systems, and mobile computing technologies, thus paving the way for a more connected and intelligent future [29].
Moreover, the impact of mobile computing technologies on wireless communication systems has been significant in recent years. The integration of mobile computing technologies into wireless communication systems has led to the creation of smart and connected devices that can communicate with each other, resulting in new opportunities for innovation and growth in various industries. The rise of the IoT has been a key driver for this development, as more and more devices are being connected to the internet to collect, process, and exchange data. This has created the need for wireless communication systems that can support many devices, have low power consumption, and have low latency. To meet these requirements, new mobile computing technologies, such as 5G, have been developed to improve wireless communication capabilities and support the growth of IoT applications. The combination of IoT and mobile computing technologies has enabled the development of new use cases, such as smart homes, smart cities, and connected vehicles, among others. Overall, the impact of mobile computing technologies on wireless communication systems has been transformative, providing new opportunities and driving innovation in a variety of industries. In conclusion, mobile computing technologies play a key role in enabling the IoT and its various applications and will continue to shape the future of IoT and other emerging technologies [37, 38].
3.3 ML in IoT-enabled wireless communication
Traditional optimization techniques have been investigated in the literature for optimizing wireless communication systems. But there are limitations such as the complexity of the techniques such as convex optimization, secondly, no closed-form formula is usually available. Moreover, traditional techniques are applied to earlier communication systems with relatively fewer variables and a better degree of freedom. Nonetheless, for modern wireless communication systems traditional optimization techniques are either impractical or nearly impossible to investigate or relied upon. A sample optimization problem has been depicted in Eq. 1. The overall system’s data rate is being enhanced subject to the fulfilment of two constraints. Namely, the bit error and transmit power. The mathematically constrained optimization problem for the communication system can be given as:
$\max R_{\text {Total }}=\sum_{i=1}^{N_{s c}} r_i$ (1)
such that, $B E R_{\text {Total }} \leq B E R_T$
and
$P_{\text {Total }} \leq \sum_{i=1}^{N_{s c}} P_i<P_T$ (2)
where, $r_i=\left(\log _2(M)\right)_i R_{c, i}$ the bit rate of the ith subcarrier, which is a product of code rate and modulation order used, PT is the available power and BERT is the target BER that depends upon the quality of service (QoS) or application requirements while Nsc is several subcarriers in NOMA.
Here comes ML which is a subfield of artificial intelligence (AI) that provides systems with the ability to automatically improve their performance through experience. In the context of IoT-enabled wireless communication, ML has the potential to greatly enhance the performance and efficiency of these networks by optimizing various aspects such as network management, resource allocation, and security [39]. This can be achieved using ML algorithms such as supervised and unsupervised learning, deep learning, and reinforcement learning. In supervised learning, the algorithms are trained on a labelled dataset and the goal is to make predictions on new, unseen data. In unsupervised learning, the algorithms work with an unlabeled dataset and the goal is to discover patterns or structures in the data. In reinforcement learning, the algorithms learn through trial-and-error interactions with an environment [40].
These algorithms can be applied to various problems encountered in IoT wireless communication systems such as energy efficiency, data accuracy, and network reliability. By leveraging ML, IoT-enabled wireless communication systems can better handle the large amounts of data generated by IoT devices and provide more robust and efficient communication services. ML is used in a wide range of applications, including image and speech recognition, natural language processing, recommendation systems, and predictive modeling. It has revolutionized many industries, including healthcare, finance, marketing, and transportation, and continues to play a crucial role in the development of AI and the IoT such as medical IoT (IoMT) [41, 42].
ML has a significant role in wireless communication, particularly in the design and optimization of communication systems. ML techniques are used to tackle complex problems in wireless communication, such as interference management, resource allocation, and network optimization. ML has also played a crucial role in the development of 5G wireless communication networks. With the increase in the number of connected devices and the growing demand for high-speed data transfer, 5G networks require sophisticated techniques to optimize performance and ensure efficient utilization of resources. ML algorithms are used in 5G networks to perform functions such as network slicing, traffic management, and congestion control [43-45].
ML has a key role in wireless communication, providing new and innovative solutions to complex problems in the field. It is expected to continue to play a crucial role in the evolution of wireless communication networks, especially with the advent of the IoT and the growing demand for high-speed data transfer. ML is a subfield of AI that focuses on the development of algorithms and models that can learn from and make predictions on data. ML algorithms use statistical techniques to model and understand the relationships between the input data and output predictions, enabling the models to improve over time with experience [46].
Moreover, ML can also help in reducing the complexity of network management by enabling autonomous decision-making, reducing the need for manual intervention, and enabling real-time responses to change network conditions. Additionally, ML can also be used to detect and prevent security threats by analyzing the behaviour of IoT devices and detecting anomalies in real-time. This helps to ensure the security and privacy of sensitive data and protects the network from potential cyber-attacks [47].
Another important aspect where ML can play a crucial role is in optimizing the utilization of network resources. ML algorithms can be used to predict network congestion and dynamically allocate network resources such as bandwidth and power to the devices that need it most. This results in improved network performance and reduces the need for manual intervention. ML has the potential to significantly improve the performance and efficiency of IoT-enabled wireless communication systems. The integration of ML with IoT and wireless communication technologies is a rapidly growing area of research, and numerous advancements have been made in recent years. Though, there is still much room for improvement, and ongoing research efforts are aimed at further refining the use of ML in these systems and developing new ML-based solutions for the various challenges faced by IoT-enabled wireless communication [48].
3.4 Summary of literature review on ML in wireless communication
This study has a specific aim to evaluate the impact of ML techniques in the upcoming generation of wireless networks while considering the IoT as a crucial factor. To achieve this aim, a systematic review of the existing literature is conducted. This study will provide an in-depth analysis of the role of ML in next-generation wireless networks and its potential impact on the design and optimization of these networks, particularly in the context of IoT devices. The systematic review of the literature enables us to gain a comprehensive understanding of the current state of the art in ML-based wireless communication and the opportunities and challenges in this area. The results of this study will provide valuable insights for researchers, engineers, and practitioners in the field of wireless communication and help in future research and development in this area. From the literature review, the following can be summarized.
(1) IoTs have been becoming an essential component in information and communication technologies because of the tremendously growing popularity of wearable and sensory devices.
(2) Communication systems have been evolving rigorously to meet the expectations of the demanding information and communication technologies of the current era.
(3) ML has been playing a significant role in optimizing the communication systems utilization and fulfilment of the enhanced speed and data rate needs.
(4) Together the IoT and communication systems have been obtaining the limitless benefits of ML in various aspects whether it is radio resources utilization or elasticity of the demand.
Moreover, Table 2 contains a summary of the selected literature for ML in wireless communication systems such as device-to-device (D2D) communication emphasizing radio resource optimization such as for spectrum and energy efficiency (EE).
Likewise, Table 3 summarizes the studies involving ML such as Gaussian Radial Basis Function (GRBF) neural networks, Fuzzy Rule-Based System (FRBS) and Genetic algorithms (GA) in adaptive communication.
Similarly, Table 4 summarizes ML in the NOMA-based systems in the power domain (PD) as well as code domain (CD) for various radio networks.
Table 2. Machine Learning based resource management
|
Ref |
Objective |
Method |
Conclusion |
|
[8] |
A power allocation scheme is utilized to optimize the D2D transmit power and maximize the EE |
ML-based power control algorithm |
It was shown that the spectrum and energy efficiency of a network can be enhanced by maximizing EE and optimizing the transmit power. |
|
[9] |
To maximize the sum throughput of D2D links, while at the same time ensuring the QoS |
Deep Reinforcement Learning (DRL) |
The simulation results reveal that POPS outperforms DDPG, DDQN, and DQN by 16.67%, 24.98%, and 59.09%. |
|
[10] |
Optimize system spectral efficiency |
Joint utility and strategy estimation-based learning |
The proposal can achieve near-optimal performance in a distributed manner |
|
[11] |
Optimize a long-term utility that is related to task execution delay, task queuing delay, and so on. |
DRL |
Improved computation offloading performance significantly compared to several baseline policies |
|
[12] |
Optimize system sum rate |
K-nearest neighbours (KNN) |
Raise system performance compared to a state-of-the-art approach. |
|
[13] |
BDMA can overcome the scarcity of time and frequency to share spectrum and OFDM is used as 5G modulation techniques. |
Beam Division Multiple Access (BDMA) |
Does not support adaptivity which is a feature of OFDM |
Table 3. Machine Learning in adaptive communication
|
Ref |
Objective |
Method |
Conclusion |
|
[14] |
ACM using product code and QAM |
FRBS |
Flat power distribution |
|
[15] |
Adaptive modulation |
Fuzzy Logic |
Only adaptive modulation |
|
[16] |
ACM and power |
FRBS and GA. |
Complex system |
|
[17] |
ACM and power using GA and product codes with QAM compared to the water-filling principle |
FRBS, Water-filling principle, GA |
Huge complexity in decoding product codes |
|
[18] |
Adaptive communication |
GRBF Neural Network |
For satellite communication only. |
|
[19] |
Adaptive communication |
FRBS and differential evolution algorithm |
For satellite communication only. |
Table 4. Machine Learning in NOMA systems
|
Ref |
Objective |
Method |
Conclusion |
|
[20] |
Optimal power allocations for m-user uplink/ downlink NOMA systems |
Evolutionary computing |
CD not considered |
|
[21] |
An overview of the latest NOMA research and innovations and applications. |
Survey on various ML NOMA applications |
Missing ACM rather than surveying challenges and trends. |
|
[22] |
PD multiplexing NOMA with an emphasis on amalgams of multiple antenna (MIMO) techniques and NOMA |
Survey on various ML methods in PD-multiplexing-aided NOMA |
Focused on MIMO and NOMA mainly while ACM was missing. |
|
[23] |
CD-NOMA performance was found better than classical ALOHA |
Compare the performance of CD-NOMA with classical ALOHA protocol. |
Focused on CD NOMA while PD NOMA was not considered |
|
[24] |
Power allocation policies are discussed for the proposed scheme |
ML in NOMA for centralized radio networks |
The application of FD in NOMA has been studied |
|
[25] |
The superiority of NOMA-enabled F-RANs over conventional OMA-enabled F-RANs is verified. |
ML was used as monotonic optimization approach |
Limited to adaptative power |
|
[26] |
Investigated the error performance of NOMA schemes in the presence of channel estimation errors in addition to imperfect SIC |
Derive exact bit error probabilities (BEPs) in closed forms and technical analysis is validated via simulations |
Limited to optimum power allocation |
The role of ML in IoT-enabled next-generation wireless communication systems is a complex and dynamic field, with several challenges that need to be addressed. Some of the main challenges include:
Despite these challenges, there are huge opportunities in applying ML for the IoT in next-generation wireless communication systems are numerous and include:
In conclusion, the integration of Machine Learning techniques with IoT and wireless communication systems has the potential to bring about significant advancements in the next generation of wireless networks. But, to fully realize the potential of ML for IoT in wireless communication, several challenges need to be addressed, including the development of algorithms that can effectively process large amounts of data in real-time, the creation of secure and reliable communication infrastructure, and the design of efficient and effective ML models. However, despite the numerous benefits, there are also several challenges associated with applying ML in IoT-based systems. These challenges include data privacy and security, computational complexity, and interpretability. To fully realize the potential of ML in IoT-based systems, it is crucial that these challenges are addressed and overcome. Further research in this area can help to expand the capabilities of ML and bring in the next generation of wireless communication systems that are more efficient, secure, and scalable.
It can further be concluded that next-generation networks are among the hottest areas of research where ML can be incorporated to solve complex problems more adequately. Nonetheless, ML techniques exhibit inherent complexity in the training phase, more research is needed to address this problem and make the solution real time. In the IoT inclusion, fog and cloud computing becomes more evident and consequently, the problems become more diversified and multifaceted. In this case, different variants of ML can be investigated such as transfer learning, federated and fusion-based learning [49, 50]. That is still an open area of research to comprehend ML-based optimization in next-generation wireless networks, especially in the IoT and cloud computing paradigms. In particular smart applications, such as wearables, smart homes, smart cities and industrial automation such as Industry 4.0 and Healthcare 5.0 are a few among many potential application areas.
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