Abstract
The Fifth Generation (5G) mobile networks use millimeter waves (mmWaves) to offer gigabit data rates. However, unlike microwaves, mmWave links are prone to user and topographic dynamics. They easily get blocked and end up forming irregular cell patterns for 5G. This in turn causes too early, too late, or wrong handoffs (HOs). To mitigate HO challenges, sustain connectivity, and avert unnecessary HO, we propose an HO scheme based on a jump Markov linear system (JMLS) and deep reinforcement learning (DRL). JMLS is widely known to account for abrupt changes in system dynamics. DRL likewise emerges as an artificial intelligence technique for learning highly dimensional and time-varying behaviors. We combine the two techniques to account for time-varying, abrupt, and irregular changes in mmWave link behavior by predicting likely deterioration patterns of target links. The prediction is optimized by meta training techniques that also reduce training sample size. Thus, the JMLS–DRL platform formulates intelligent and versatile HO policies for 5G. When compared to a signal and interference noise ratio (SINR) and DRL-based HO scheme, our HO scheme becomes more reliable in selecting reliable target links. In particular, our proposed scheme is able to reduce wasteful HO to less than 5% within 200 training episodes compared to the DRL-based HO scheme that needs more than 200 training episodes to get to less than 5%. It supports longer dew time between HOs and high sum rates by ably averting unnecessary HOs with almost half the HOs compared to a DRL-based HO scheme.
Highlights
Introduction iationsFifth Generation (5G) mobile users need uninterrupted connectivity while consuming large amounts of data and media content when commuting [1]
Open artificial intelligence (AI) Gym is an reinforcement learning (RL) development that is integrable with the ns-3 simulator; it supports teaching agents for a variety of network applications including those in ns-3
This paper proposed a new HO scheme given the distinct propagation characteristics of millimeter waves (mmWaves) in a HetNet structure
Summary
The surging role/potential of mmWave bands in mobile networks such as 5G/beyond cannot be ignored. The authors in took four approaches to tackle the crucial problem of distance limitation owing to high spreading loss and molecular absorption that often limit the mmWave transmission distance and coverage range These were a physical layer distance-aware design, ultra-massive MIMO communication, reflect arrays, and intelligent surfaces. Sensors 2022, 22, 746 learning (MARL) algorithm based on the proximal policy optimization (PPO) method, by introducing centralized training with a decentralized execution framework In all these schemes, highly mobile and dynamic users were hardly considered. Requires accurate channel state information (CSI) to converge In such cases, the authors in [26] argued that inaccurate training gradually cripples the accuracy of predictions, at low signal-to-noise ratios (SNRs). It is worth reiterating that none of the mentioned works analyzed the deterioration pattern of mmWaves to make an HO decision or utilized multiple users with very different levels of impact on mmWave propagation characteristics; they were all designed to operate in a single frequency band or with one user type
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