A Deep Learning‐Based Power Control and Consensus Performance of Spectrum Sharing in the CR Network

Abstract

The cognitive radio network (CRN) is aimed at strengthening the system through learning and adjusting by observing and measuring the available resources. Due to spectrum sensing capability in CRN, it should be feasible and fast. The capability to observe and reconfigure is the key feature of CRN, while current machine learning techniques work great when incorporated with system adaption algorithms. This paper describes the consensus performance and power control of spectrum sharing in CRN. (1) CRN users are considered noncooperative users such that the power control policy of a primary user (PU) is predefined keeping the secondary user (SU) unaware of PU’s power control policy. For a more efficient spectrum sharing performance, a deep learning power control strategy has been developed. This algorithm is based on the received signal strength at CRN nodes. (2) An agent-based approach is introduced for the CR user’s consensus performance. (3) All agents reached their steady-state value after nearly 100 seconds. However, the settling time is large. Sensing delay of 0.4 second inside whole operation is identical. The assumed method is enough for the representation of large-scale sensing delay in the CR network.

Sixth Generation (6G) Cognitive Radio Network (CRN) Application, Requirements, Security Issues, and Key Challenges

“…Cascade and blind channel estimation could be helping SRS applications for the help to spectrum sensing (SS)) [94] (v) Exchange protocol for wireless sensor networks [95] (data sending nodes should be up-to-date by a central entity about already present channel) (vi) For CR, sensing spatial dimension [96,97] (in noncooperation manner with PU, estimation beam of PU become a challenge, even to know PU, SU beam adjustment is also challenging because of secondary user transmitter inevitable interference to SU receiver) (vii) SS binary decision complication indirection of smart spectrum sensing [98] (viii) In CR communication technology, there is a challenge of future public safety [99,100] such as license plate detection, web access, and video streaming (ix) The primary key challenge in CRN is the demonstration of CRN technology, assess, and failure to conclusively test at real-world installation scenario and scale. Physical installation needs a high resolution, speedy signal processor, and high sampling rate for CR applications in cooperative sensing [101,102] (x) Challenge when SU does not see PU [12] (xi) During SUs transmission, PUs can claim their licensed band, and in this process, there may be the challenge of sensing duration as studied in [12,103] (xii) Economics and spectrum policy challenges [104] such as the challenge in conveying energetic spectrum standards are that of essentially progressing spectrum utilization effectiveness without losing the benefits of inactive spectrum assignment [27].…”

“…Although millimeter-wave can offer Gbps data transmission in 5G, it will require a Tbps data rate in 6G for applications [7,8] such as 3D high-quality videos, virtual reality, augmented reality, alternative frequency bands, and THz may be contender bands [9,10]. With the use of extraordinary heterogeneous networks, number of antennas in large amount, new service diverse communication situation, and wide bandwidth, there will be new access of smart applications in 6G with the assistance of machine learning [11,12] and artificial intelligence technologies. There is an automation level for network performance improvement in several forms, such as quality of security, quality of services, fault management, experience quality, and energy efficiency [13][14][15].…”

Enhanced Scalable Downlink Power Allocation for Centralized Scenarios in Cell-free Massive MIMO

“…So, Large-scale fading (β LK [dB]) is the difference in channel gain in urban deployment B d [dB] and noise power (ρ n ). One of the main goals of this paper is to show that implementing a deep neural network by examining large-scale propagation and user position has a significant impact on power control [1]. As earlier mentioned, concentrating on the supervised learning-based Deep Neural Network (DNN) approach in which data are labeled for model training (offline) requires an excellent way to allocate power or predict the outputs.…”

Spectrum Estimation and Optimal Secondary User Selection in Cognitive Radio Networks

“…A survey of spectrum allocation using machine learning was presented in [11]. With the CRN focused or positioned at reinforcing the system via learning and updating by discovering and estimating the resource availability, a consensus performance and spectrum sharing power control mechanism in CRN was proposed in [12]. With this largescale sensing delay in the CRN was focused.…”