Artificial intelligence for place-time convolved wireless communication networks /

Autor(es):
Kumar, Ambuj
Editor: Geneva : International Telecommunication Union, 2018Descripción: 9 pTipo de contenido: texto (visual)
Tipo de medio: electrónico
Tipo de soporte: recurso en línea
Tema(s): Tecnologías habilitadoras digitales | Science and TechnologyRecursos en línea: Acceso al documento En: ITU Journal: ICT Discoveries Vol. 2018, no. 1, p. 85-93Resumen: Previous work have brought to light the dynamic and variable motions of potential network users, and other environmental factors, are the perennial and perpetual challenge to present and future wireless communications. The phenomena coined as place time capacity (PTC), and place time coverage (PTCo), with both collectively known as place time coverage and capacity (PTC.2), are derived as the outcomes of dynamics that can be expressed as the functions of place and time. These phenomena degrade the efficiency of any wireless communication network (WCN) to that lowest point, from where, a network service provider (NSP) may not have any choice but to revamp the network. Artificial intelligence (AI), on the other hand, has been striding profoundly for the past several decades, fanning out its influence in various sectors of scientific and technological developments. However, AI is almost absent in the area of WCN dimensioning and optimization, especially for place-time events. This paper revisits the two place-time functions as WCN phenomena and, with the backdrop of these aspects, shall investigate the inevitable need for AI in WCNs, as well as demonstrating how AI can be part of present and future wireless communications.
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Previous work have brought to light the dynamic and variable motions of potential network users, and other environmental factors, are the perennial and perpetual challenge to present and future wireless communications. The phenomena coined as place time capacity (PTC), and place time coverage (PTCo), with both collectively known as place time coverage and capacity (PTC.2), are derived as the outcomes of dynamics that can be expressed as the functions of place and time. These phenomena degrade the efficiency of any wireless communication network (WCN) to that lowest point, from where, a network service provider (NSP) may not have any choice but to revamp the network. Artificial intelligence (AI), on the other hand, has been striding profoundly for the past several decades, fanning out its influence in various sectors of scientific and technological developments. However, AI is almost absent in the area of WCN dimensioning and optimization, especially for place-time events. This paper revisits the two place-time functions as WCN phenomena and, with the backdrop of these aspects, shall investigate the inevitable need for AI in WCNs, as well as demonstrating how AI can be part of present and future wireless communications.

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