Abstract
This paper surveys Artificial Intelligence (AI) methods for acquiring and managing context-of-operation awareness of radio communication nodes, links, and networks. The meaning and significance of context information and suitability of Machine Learning (ML) methods for the enrichment of context information is discussed. A number of context features are considered in this regard and thorough analysis on which ML methods are suitable to which part of context learning is provided. The added value of the paper is the presentation of a synthesized framework of context-information processing, sharing, and management in a radio communication network by delineating a network-embedded subsystem for this management. Recommendations for a future AI/ML-based radio communication system architectures are also provided.
Highlights
I N the era of ubiquitous information access and pervasive communication networks, systems and nodes are needed to be aware of their context of operation, utilizing information on ambient networks, links, devices and applications
The context information itself consists of different parts/components, each of which affects the individual steps of the decision making process in a different way
Various parts that constitute the context are related to the following levels: (i) the hardware platform, which poses specific hardware constraints and implementation issues, (ii) radio environment conditions in terms of location-specific parameters, wireless channel quality, spectrum availability, other-users characteristics and signal features, traffic patterns, interference levels, etc., (iii) required performance (QoS) parameters that can be identified in all layers of the system protocol stack, and are considered to be the basis for the evaluation of decisions made, (iv) network management policies as a set of rules used to control the behavior of nodes, manage available resources, regulate interference to other deployed systems, obtain identified trade-offs, etc
Summary
I N the era of ubiquitous information access and pervasive communication networks, systems and nodes are needed to be aware of their context of operation, utilizing information on ambient networks, links, devices and applications. Regardless of the exact classification of context data, it is worth summarizing jointly the examples of radio context information available in contemporary wireless systems (mainly cellular networks, and wireless local and personal area networks) They are typically used for describing the observed or predicted quality of signal, assessing the measured signal power, defining the best method of adaptive signal processing or just for describing the generic system setup. VARIOUS DOMAINS FOR CONTEXT INFORMATION GATHERING Table 1 gathers commonly used metrics or parameters for defining the instantaneous radio communication context They may be broadly classified as information related to power management (such as received signal strength, RSS, signal to noise ratio, SNR), channel quality measurements (such as channel quality indicator, CQI, channel state information, CSI), network configuration (such as absolute radio frequency channel number, ARFCN), selected signal processing schemes (such as rank indicator, RI or MCS) or traffic characterization (e.g., maximum bit rate, MBR, QoS class identifier, QCI). Please note that wideband spectrum sensing may be treated as a first step in gathering context information - once the activity of PUs is detected, a more detailed narrowband, AI/ML-based spectrum sensing algorithm can be applied
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