Reinforcement Learning Based 5G Enabled Cognitive Radio Networks

Puspita, Ratih Hikmah; Shah, Syed Danial Ali; Lee, Gyu-min; Roh, Byeong‐hee; Oh, Jimyeong; Kang, Sung-Jin

doi:10.1109/ictc46691.2019.8939986

Cited by 10 publications

(6 citation statements)

References 17 publications

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“…For this purpose, some commonly used ML schemes are discussed ahead. RL (reinforcement learning) is a fluky methodology, and the algorithm is robust [40][41][42][43]; however, this architecture does not guarantee improved latencies and delays. erefore, this technique is not recommended for applications where robust and quick decisions are required.…”

Section: Contributionsmentioning

confidence: 99%

Using an Efficient Technique Based on Dynamic Learning Period for Improving Delay in AI-Based Handover

Majid

Shah

Marwat

et al. 2021

Mobile Information Systems

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The future high-speed cellular networks require efficient and high-speed handover mechanisms. However, the traditional cellular handovers are based upon measurements of target cell radio strength which requires frequent measurement gaps. During these measurement windows, data transmission ceases each time, while target bearings are measured causing serious performance degradation. Therefore, prediction-based handover techniques are preferred in order to eliminate frequent measurement windows. Thus, this work proposes an ultrafast and efficient XGBoost-based predictive handover technique for next generation mobile communications. The ML algorithm in general prefers 70–30% of training and test data, respectively. However, always obtaining 70% of training samples in mobile communications is challenging because the channel remains correlated within coherence time only. Therefore, collecting training samples beyond coherence time limits performance and adds delay; thus, the proposed work trains the model within coherence time where this fixed data split of 70–30% makes the model exceed coherence time. Despite the fact that the proposed model gets starved of required training samples, still there is no loss in predication accuracy. The test results show a maximum delay improvement of up to 596 ms with enhanced performance efficiency of 68.70% depending upon the scenario. The proposed model reduces delay and improves efficiency by having an appropriate training period; thus, the intelligent technique activates faster with improved accuracy and eliminates delay in the algorithm to predict mmWaves’ signal strength in contrast to actually measuring them.

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Section: Contributionsmentioning

confidence: 99%

Using an Efficient Technique Based on Dynamic Learning Period for Improving Delay in AI-Based Handover

Majid

Shah

Marwat

et al. 2021

Mobile Information Systems

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“…Contrarywise, Puspita et al ( 2019) conducted a study on cognitive radio that involved the use of RL algorithm (13). Their research presented insight on an RL-based cognitive radio network system to provide the most efficient frequency to use for the most efficient spectrum management.…”

Section: Literature Reviewmentioning

confidence: 99%

Reinforcement Learning-Based Television White Space Database

Pakzad

Manuel

et al. 2021

Baghdad Sci.J

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Television white spaces (TVWSs) refer to the unused part of the spectrum under the very high frequency (VHF) and ultra-high frequency (UHF) bands. TVWS are frequencies under licenced primary users (PUs) that are not being used and are available for secondary users (SUs). There are several ways of implementing TVWS in communications, one of which is the use of TVWS database (TVWSDB). The primary purpose of TVWSDB is to protect PUs from interference with SUs. There are several geolocation databases available for this purpose. However, it is unclear if those databases have the prediction feature that gives TVWSDB the capability of decreasing the number of inquiries from SUs. With this in mind, the authors present a reinforcement learning-based TVWSDB. Reinforcement learning (RL) is a machine learning technique that focuses on what has been done based on mapping situations to actions to obtain the highest reward. The learning process was conducted by trying out the actions to gain the reward instead of being told what to do. The actions may directly affect the rewards and future rewards. Based on the results, this algorithm effectively searched the most optimal channel for the SUs in query with the minimum search duration. This paper presents the advantage of using a machine learning approach in TVWSDB with an accurate and faster-searching capability for the available TVWS channels intended for SUs.

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“…Initially CRN observes the present state of SU at its i th intervention. Then after interruption CR node chooses a handoff activity for (i+1) th interruption caused by PU again based on present state from Q table [6,13]. So SU makes a move to next state 𝑠𝑗,𝑖+1 as a result MOS is obtained as reward and further updates the Q table [32].…”

Section: States Of Sus Associationsmentioning

confidence: 99%

Reinforcement Learning Based Handoff Mechanism in Cooperative Cognitive Radio Networks

Mathai¹,

Indumathi²

2021

DSA

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The spectrum handoff (SH) is a dynamic spectrum access technique which ensures effective channel utilization, fair resource allocation, as well as uninterrupted real-time connection. Facilitating SH across traffics of dissimilar characteristics in Cognitive Radio Networks (CRNs) is posing difficulty due to manifold interventions from Primary Users (PUs), disagreement among Secondary Users (SUs) and diversified Quality of Experience (QoE) demand. Here, we consider effective channel selection strategy (CSS) and put forward a learning-based handoff scheme to enhance QoE demand of users by the introduction of docition idea. A PU prioritized Markov method is introduced to represent the communications between PUs and SUs for even channel access. The reinforcement learning (RL) is applied to CSS to carry out proper channel selection. Numerical outcomes projects that proposed queuing model, suggested learning based handoff scheme and docitive learning enhances the quality of service by maintaining the average MOS of 3.6.

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Reinforcement Learning Based 5G Enabled Cognitive Radio Networks

Cited by 10 publications

References 17 publications

Using an Efficient Technique Based on Dynamic Learning Period for Improving Delay in AI-Based Handover

Using an Efficient Technique Based on Dynamic Learning Period for Improving Delay in AI-Based Handover

Reinforcement Learning-Based Television White Space Database

Reinforcement Learning Based Handoff Mechanism in Cooperative Cognitive Radio Networks

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