10–50 m
MmWave is a very high band spectrum between 30 to 300 GHz. As it is a significantly less used spectrum, it provides very high-speed wireless communication. MmWave offers ultra-wide bandwidth for next-generation mobile networks. MmWave has lots of advantages, but it has some disadvantages, too, such as mmWave signals are very high-frequency signals, so they have more collision with obstacles in the air which cause the signals loses energy quickly. Buildings and trees also block MmWave signals, so these signals cover a shorter distance. To resolve these issues, multiple small cell stations are installed to cover the gap between end-user and base station [ 18 ]. Small cell covers a very shorter range, so the installation of a small cell depends on the population of a particular area. Generally, in a populated place, the distance between each small cell varies from 10 to 90 meters. In the survey [ 20 ], various authors implemented small cells with massive MIMO simultaneously. They also reviewed multiple technologies used in 5G like beamforming, small cell, massive MIMO, NOMA, device to device (D2D) communication. Various problems like interference management, spectral efficiency, resource management, energy efficiency, and backhauling are discussed. The author also gave a detailed presentation of all the issues occurring while implementing small cells with various 5G technologies. As shown in the Figure 7 , mmWave has a higher range, so it can be easily blocked by the obstacles as shown in Figure 7 a. This is one of the key concerns of millimeter-wave signal transmission. To solve this issue, the small cell can be placed at a short distance to transmit the signals easily, as shown in Figure 7 b.
Pictorial representation of communication with and without small cells.
Beamforming is a key technology of wireless networks which transmits the signals in a directional manner. 5G beamforming making a strong wireless connection toward a receiving end. In conventional systems when small cells are not using beamforming, moving signals to particular areas is quite difficult. Beamforming counter this issue using beamforming small cells are able to transmit the signals in particular direction towards a device like mobile phone, laptops, autonomous vehicle and IoT devices. Beamforming is improving the efficiency and saves the energy of the 5G network. Beamforming is broadly divided into three categories: Digital beamforming, analog beamforming and hybrid beamforming. Digital beamforming: multiuser MIMO is equal to digital beamforming which is mainly used in LTE Advanced Pro and in 5G NR. In digital beamforming the same frequency or time resources can be used to transmit the data to multiple users at the same time which improves the cell capacity of wireless networks. Analog Beamforming: In mmWave frequency range 5G NR analog beamforming is a very important approach which improves the coverage. In digital beamforming there are chances of high pathloss in mmWave as only one beam per set of antenna is formed. While the analog beamforming saves high pathloss in mmWave. Hybrid beamforming: hybrid beamforming is a combination of both analog beamforming and digital beamforming. In the implementation of MmWave in 5G network hybrid beamforming will be used [ 84 ].
Wireless signals in the 4G network are spreading in large areas, and nature is not Omnidirectional. Thus, energy depletes rapidly, and users who are accessing these signals also face interference problems. The beamforming technique is used in the 5G network to resolve this issue. In beamforming signals are directional. They move like a laser beam from the base station to the user, so signals seem to be traveling in an invisible cable. Beamforming helps achieve a faster data rate; as the signals are directional, it leads to less energy consumption and less interference. In [ 21 ], investigators evolve some techniques which reduce interference and increase system efficiency of the 5G mobile network. In this survey article, the authors covered various challenges faced while designing an optimized beamforming algorithm. Mainly focused on different design parameters such as performance evaluation and power consumption. In addition, they also described various issues related to beamforming like CSI, computation complexity, and antenna correlation. They also covered various research to cover how beamforming helps implement MIMO in next-generation mobile networks [ 85 ]. Figure 8 shows the pictorial representation of communication with and without using beamforming.
Pictorial Representation of communication with and without using beamforming.
Mobile Edge Computing (MEC) [ 24 ]: MEC is an extended version of cloud computing that brings cloud resources closer to the end-user. When we talk about computing, the very first thing that comes to our mind is cloud computing. Cloud computing is a very famous technology that offers many services to end-user. Still, cloud computing has many drawbacks. The services available in the cloud are too far from end-users that create latency, and cloud user needs to download the complete application before use, which also increases the burden to the device [ 86 ]. MEC creates an edge between the end-user and cloud server, bringing cloud computing closer to the end-user. Now, all the services, namely, video conferencing, virtual software, etc., are offered by this edge that improves cloud computing performance. Another essential feature of MEC is that the application is split into two parts, which, first one is available at cloud server, and the second is at the user’s device. Therefore, the user need not download the complete application on his device that increases the performance of the end user’s device. Furthermore, MEC provides cloud services at very low latency and less bandwidth. In [ 23 , 87 ], the author’s investigation proved that successful deployment of MEC in 5G network increases the overall performance of 5G architecture. Graphical differentiation between cloud computing and mobile edge computing is presented in Figure 9 .
Pictorial representation of cloud computing vs. mobile edge computing.
Security is the key feature in the telecommunication network industry, which is necessary at various layers, to handle 5G network security in applications such as IoT, Digital forensics, IDS and many more [ 88 , 89 ]. The authors [ 90 ], discussed the background of 5G and its security concerns, challenges and future directions. The author also introduced the blockchain technology that can be incorporated with the IoT to overcome the challenges in IoT. The paper aims to create a security framework which can be incorporated with the LTE advanced network, and effective in terms of cost, deployment and QoS. In [ 91 ], author surveyed various form of attacks, the security challenges, security solutions with respect to the affected technology such as SDN, Network function virtualization (NFV), Mobile Clouds and MEC, and security standardizations of 5G, i.e., 3GPP, 5GPPP, Internet Engineering Task Force (IETF), Next Generation Mobile Networks (NGMN), European Telecommunications Standards Institute (ETSI). In [ 92 ], author elaborated various technological aspects, security issues and their existing solutions and also mentioned the new emerging technological paradigms for 5G security such as blockchain, quantum cryptography, AI, SDN, CPS, MEC, D2D. The author aims to create new security frameworks for 5G for further use of this technology in development of smart cities, transportation and healthcare. In [ 93 ], author analyzed the threats and dark threat, security aspects concerned with SDN and NFV, also their Commercial & Industrial Security Corporation (CISCO) 5G vision and new security innovations with respect to the new evolving architectures of 5G [ 94 ].
AuthenticationThe identification of the user in any network is made with the help of authentication. The different mobile network generations from 1G to 5G have used multiple techniques for user authentication. 5G utilizes the 5G Authentication and Key Agreement (AKA) authentication method, which shares a cryptographic key between user equipment (UE) and its home network and establishes a mutual authentication process between the both [ 95 ].
Access Control To restrict the accessibility in the network, 5G supports access control mechanisms to provide a secure and safe environment to the users and is controlled by network providers. 5G uses simple public key infrastructure (PKI) certificates for authenticating access in the 5G network. PKI put forward a secure and dynamic environment for the 5G network. The simple PKI technique provides flexibility to the 5G network; it can scale up and scale down as per the user traffic in the network [ 96 , 97 ].
Communication Security 5G deals to provide high data bandwidth, low latency, and better signal coverage. Therefore secure communication is the key concern in the 5G network. UE, mobile operators, core network, and access networks are the main focal point for the attackers in 5G communication. Some of the common attacks in communication at various segments are Botnet, message insertion, micro-cell, distributed denial of service (DDoS), and transport layer security (TLS)/secure sockets layer (SSL) attacks [ 98 , 99 ].
Encryption The confidentiality of the user and the network is done using encryption techniques. As 5G offers multiple services, end-to-end (E2E) encryption is the most suitable technique applied over various segments in the 5G network. Encryption forbids unauthorized access to the network and maintains the data privacy of the user. To encrypt the radio traffic at Packet Data Convergence Protocol (PDCP) layer, three 128-bits keys are applied at the user plane, nonaccess stratum (NAS), and access stratum (AS) [ 100 ].
In this section, various issues addressed by investigators in 5G technologies are presented in Table 13 . In addition, different parameters are considered, such as throughput, latency, energy efficiency, data rate, spectral efficiency, fairness & computing capacity, transmission rate, coverage, cost, security requirement, performance, QoS, power optimization, etc., indexed from R1 to R14.
Summary of 5G Technology above stated challenges (R1:Throughput, R2:Latency, R3:Energy Efficiency, R4:Data Rate, R5:Spectral efficiency, R6:Fairness & Computing Capacity, R7:Transmission Rate, R8:Coverage, R9:Cost, R10:Security requirement, R11:Performance, R12:Quality of Services (QoS), R13:Power Optimization).
Approach | R1 | R2 | R3 | R4 | R5 | R6 | R7 | R8 | R9 | R10 | R11 | R12 | R13 | R14 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Panzner et al. [ ] | Good | Low | Good | - | Avg | - | - | - | - | - | - | - | - | - |
Qiao et al. [ ] | - | - | - | - | - | - | - | Avg | Good | Avg | - | - | - | - |
He et al. [ ] | Avg | Low | Avg | - | - | - | - | - | - | - | - | - | - | - |
Abrol and jha [ ] | - | - | Good | - | - | - | - | - | - | - | - | - | - | Good |
Al-Imari et al. [ ] | - | - | - | - | Good | Good | Avg | - | - | - | - | - | - | - |
Papadopoulos et al. [ ] | Good | Low | Avg | - | Avg | - | - | - | - | - | - | - | - | - |
Kiani and Nsari [ ] | - | - | - | - | Avg | Good | Good | - | - | - | - | - | - | - |
Beck [ ] | - | Low | - | - | - | - | - | Avg | - | - | - | Good | - | Avg |
Ni et al. [ ] | - | - | - | Good | - | - | - | - | - | - | Avg | Avg | - | - |
Elijah [ ] | Avg | Low | Avg | - | - | - | - | - | - | - | - | - | - | - |
Alawe et al. [ ] | - | Low | Good | - | - | - | - | - | - | - | - | - | Avg | - |
Zhou et al. [ ] | Avg | - | Good | - | Avg | - | - | - | - | - | - | - | - | - |
Islam et al. [ ] | - | - | - | - | Good | Avg | Avg | - | - | - | - | - | - | - |
Bega et al. [ ] | - | Avg | - | - | - | - | - | - | - | - | - | - | Good | - |
Akpakwu et al. [ ] | - | - | - | Good | - | - | - | - | - | - | Avg | Good | - | - |
Wei et al. [ ] | - | - | - | - | - | - | - | Good | Avg | Low | - | - | - | - |
Khurpade et al. [ ] | - | - | - | Avg | - | - | - | - | - | - | - | Avg | - | - |
Timotheou and Krikidis [ ] | - | - | - | - | Good | Good | Avg | - | - | - | - | - | - | - |
Wang [ ] | Avg | Low | Avg | Avg | - | - | - | - | - | - | - | - | - | - |
Akhil Gupta & R. K. Jha [ ] | - | - | Good | Avg | Good | - | - | - | - | - | - | Good | Good | - |
Pérez-Romero et al. [ ] | - | - | Avg | - | - | - | - | - | - | - | - | - | - | Avg |
Pi [ ] | - | - | - | - | - | - | - | Good | Good | Avg | - | - | - | - |
Zi et al. [ ] | - | Avg | Good | - | - | - | - | - | - | - | - | - | - | - |
Chin [ ] | - | - | Good | Avg | - | - | - | - | - | Avg | - | Good | - | - |
Mamta Agiwal [ ] | - | Avg | - | Good | - | - | - | - | - | - | Good | Avg | - | - |
Ramesh et al. [ ] | Good | Avg | Good | - | Good | - | - | - | - | - | - | - | - | - |
Niu [ ] | - | - | - | - | - | - | - | Good | Avg | Avg | - | - | - | |
Fang et al. [ ] | - | Avg | Good | - | - | - | - | - | - | - | - | - | Good | - |
Hoydis [ ] | - | - | Good | - | Good | - | - | - | - | Avg | - | Good | - | - |
Wei et al. [ ] | - | - | - | - | Good | Avg | Good | - | - | - | - | - | - | - |
Hong et al. [ ] | - | - | - | - | - | - | - | - | Avg | Avg | Low | - | - | - |
Rashid [ ] | - | - | - | Good | - | - | - | Good | - | - | - | Avg | - | Good |
Prasad et al. [ ] | Good | - | Good | - | Avg | - | - | - | - | - | - | - | - | - |
Lähetkangas et al. [ ] | - | Low | Av | - | - | - | - | - | - | - | - | - | - | - |
This survey article illustrates the emergence of 5G, its evolution from 1G to 5G mobile network, applications, different research groups, their work, and the key features of 5G. It is not just a mobile broadband network, different from all the previous mobile network generations; it offers services like IoT, V2X, and Industry 4.0. This paper covers a detailed survey from multiple authors on different technologies in 5G, such as massive MIMO, Non-Orthogonal Multiple Access (NOMA), millimeter wave, small cell, MEC (Mobile Edge Computing), beamforming, optimization, and machine learning in 5G. After each section, a tabular comparison covers all the state-of-the-research held in these technologies. This survey also shows the importance of these newly added technologies and building a flexible, scalable, and reliable 5G network.
This article covers a detailed survey on the 5G mobile network and its features. These features make 5G more reliable, scalable, efficient at affordable rates. As discussed in the above sections, numerous technical challenges originate while implementing those features or providing services over a 5G mobile network. So, for future research directions, the research community can overcome these challenges while implementing these technologies (MIMO, NOMA, small cell, mmWave, beam-forming, MEC) over a 5G network. 5G communication will bring new improvements over the existing systems. Still, the current solutions cannot fulfill the autonomous system and future intelligence engineering requirements after a decade. There is no matter of discussion that 5G will provide better QoS and new features than 4G. But there is always room for improvement as the considerable growth of centralized data and autonomous industry 5G wireless networks will not be capable of fulfilling their demands in the future. So, we need to move on new wireless network technology that is named 6G. 6G wireless network will bring new heights in mobile generations, as it includes (i) massive human-to-machine communication, (ii) ubiquitous connectivity between the local device and cloud server, (iii) creation of data fusion technology for various mixed reality experiences and multiverps maps. (iv) Focus on sensing and actuation to control the network of the entire world. The 6G mobile network will offer new services with some other technologies; these services are 3D mapping, reality devices, smart homes, smart wearable, autonomous vehicles, artificial intelligence, and sense. It is expected that 6G will provide ultra-long-range communication with a very low latency of 1 ms. The per-user bit rate in a 6G wireless network will be approximately 1 Tbps, and it will also provide wireless communication, which is 1000 times faster than 5G networks.
Author contributions.
Conceptualization: R.D., I.Y., G.C., P.L. data gathering: R.D., G.C., P.L, I.Y. funding acquisition: I.Y. investigation: I.Y., G.C., G.P. methodology: R.D., I.Y., G.C., P.L., G.P., survey: I.Y., G.C., P.L, G.P., R.D. supervision: G.C., I.Y., G.P. validation: I.Y., G.P. visualization: R.D., I.Y., G.C., P.L. writing, original draft: R.D., I.Y., G.C., P.L., G.P. writing, review, and editing: I.Y., G.C., G.P. All authors have read and agreed to the published version of the manuscript.
This paper was supported by Soonchunhyang University.
Informed consent statement, data availability statement, conflicts of interest.
The authors declare no conflict of interest.
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30 Pages Posted: 20 Aug 2024
Eskisehir Osmangazi University
The rapidly ageing population necessitates a nuanced approach to technology design that considers both its potential benefits and ethical implications. While research in ageing and technology has increased recently, a comprehensive analysis of scientific studies in this field remains incomplete. This study addresses this gap by analyzing 906 articles on ageing and technology (1992-2024) from the Web of Science (WoS) using bibliometric techniques facilitated by the Bibliometrix package in R software. By employing broader search terms (e.g., “technology,” “ageing”), we ensured a wider representation of this interdisciplinary field and revealed key contributors, collaboration patterns, and trending themes. Our findings demonstrate uneven knowledge production, with collaboration patterns tending to form closed communities. Moreover, we identified dominant areas such as gerontechnology and older adults’ perspectives, while under-explored themes like daily activities, mental health, autonomy, and social isolation emerged as critical for older population-technology interaction. We also discovered a growing focus on “innovation for older adults,” emphasizing the ongoing need for technological interventions tailored to the specific needs and interaction patterns of older individuals. These findings provide valuable insights for researchers and practitioners in the ageing-technology field, aiming to address the challenges and opportunities faced by older individuals in a technologically driven world.
Keywords: technology, ageing, older adults, scientific research, bibliometric analysis, Web of Science (WoS)
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August 21, 2024
This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility:
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by Karin Verspoor, The Conversation
Scientific discovery is one of the most sophisticated human activities. First, scientists must understand the existing knowledge and identify a significant gap. Next, they must formulate a research question and design and conduct an experiment in pursuit of an answer. Then, they must analyze and interpret the results of the experiment, which may raise yet another research question.
Can a process this complex be automated? Last week, Sakana AI Labs announced the creation of an "AI scientist"—an artificial intelligence system they claim can make scientific discoveries in the area of machine learning in a fully automated way.
Using generative large language models (LLMs) like those behind ChatGPT and other AI chatbots, the system can brainstorm, select a promising idea, code new algorithms, plot results, and write a paper summarizing the experiment and its findings, complete with references. Sakana claims the AI tool can undertake the complete lifecycle of a scientific experiment at a cost of just US$15 per paper—less than the cost of a scientist's lunch.
These are some big claims. Do they stack up? And even if they do, would an army of AI scientists churning out research papers with inhuman speed really be good news for science ?
A lot of science is done in the open, and almost all scientific knowledge has been written down somewhere (or we wouldn't have a way to "know" it). Millions of scientific papers are freely available online in repositories such as arXiv and PubMed .
LLMs trained with this data capture the language of science and its patterns. It is therefore perhaps not at all surprising that a generative LLM can produce something that looks like a good scientific paper—it has ingested many examples that it can copy.
What is less clear is whether an AI system can produce an interesting scientific paper. Crucially, good science requires novelty.
Scientists don't want to be told about things that are already known. Rather, they want to learn new things, especially new things that are significantly different from what is already known. This requires judgment about the scope and value of a contribution.
The Sakana system tries to address interestingness in two ways. First, it "scores" new paper ideas for similarity to existing research (indexed in the Semantic Scholar repository). Anything too similar is discarded.
Second, Sakana's system introduces a "peer review" step—using another LLM to judge the quality and novelty of the generated paper. Here again, there are plenty of examples of peer review online on sites such as openreview.net that can guide how to critique a paper. LLMs have ingested these, too.
Feedback is mixed on Sakana AI's output. Some have described it as producing " endless scientific slop ."
Even the system's own review of its outputs judges the papers weak at best. This is likely to improve as the technology evolves, but the question of whether automated scientific papers are valuable remains.
The ability of LLMs to judge the quality of research is also an open question. My own work (soon to be published in Research Synthesis Methods ) shows LLMs are not great at judging the risk of bias in medical research studies, though this too may improve over time.
Sakana's system automates discoveries in computational research, which is much easier than in other types of science that require physical experiments. Sakana's experiments are done with code, which is also structured text that LLMs can be trained to generate.
AI researchers have been developing systems to support science for decades. Given the huge volumes of published research, even finding publications relevant to a specific scientific question can be challenging.
Specialized search tools make use of AI to help scientists find and synthesize existing work. These include the above-mentioned Semantic Scholar, but also newer systems such as Elicit , Research Rabbit , scite and Consensus .
Text mining tools such as PubTator dig deeper into papers to identify key points of focus, such as specific genetic mutations and diseases, and their established relationships. This is especially useful for curating and organizing scientific information.
Machine learning has also been used to support the synthesis and analysis of medical evidence, in tools such as Robot Reviewer . Summaries that compare and contrast claims in papers from Scholarcy help to perform literature reviews.
All these tools aim to help scientists do their jobs more effectively, not to replace them.
While Sakana AI states it doesn't see the role of human scientists diminishing, the company's vision of "a fully AI-driven scientific ecosystem" would have major implications for science.
One concern is that, if AI-generated papers flood the scientific literature, future AI systems may be trained on AI output and undergo model collapse . This means they may become increasingly ineffectual at innovating.
However, the implications for science go well beyond impacts on AI science systems themselves.
There are already bad actors in science, including "paper mills" churning out fake papers . This problem will only get worse when a scientific paper can be produced with US$15 and a vague initial prompt.
The need to check for errors in a mountain of automatically generated research could rapidly overwhelm the capacity of actual scientists. The peer review system is arguably already broken, and dumping more research of questionable quality into the system won't fix it.
Science is fundamentally based on trust. Scientists emphasize the integrity of the scientific process so we can be confident our understanding of the world (and now, the world's machines) is valid and improving.
A scientific ecosystem where AI systems are key players raises fundamental questions about the meaning and value of this process, and what level of trust we should have in AI scientists. Is this the kind of scientific ecosystem we want?
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While research in ageing and technology has increased recently, a comprehensive analysis of scientific studies in this field remains incomplete. This study addresses this gap by analyzing 906 articles on ageing and technology (1992-2024) from the Web of Science (WoS) using bibliometric techniques facilitated by the Bibliometrix package in R ...
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Abstract We report the results of the second Charpy Interlaboratory Comparison between NIST (National Institute of Standards and Technology, Boulder, Colorado, USA) and ATMS (Anand Testing Machine Services, Kabnur, India).
This article examines the determinants of India's information and communications technology (ICT) access. We use the second wave of the India Human Development Survey (IHDS) of 2011-2012, a nationally representative survey at the individual level, and find that ICT ownership and usage are abysmally low for women compared to men.
Download full-text PDF. Read full-text. ... Artefact A can print 5000 word on paper during one minute. ... The Technology Research Methodology consists of three main activities and is an iterative ...
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AI research may exacerbate existing problems. While Sakana AI states it doesn't see the role of human scientists diminishing, the company's vision of "a fully AI-driven scientific ecosystem" would have major implications for science.. One concern is that, if AI-generated papers flood the scientific literature, future AI systems may be trained on AI output and undergo model collapse.
Download full-text PDF ... it can be stated that usage of technology has proven to be advantageous to the individuals. The main aspects that have been taken into account in this research paper ...