Pattern Based Mobility Management in 5G Networks With a Game Theoretic-Jump Markov Linear System Approach

Chiputa, Masoto; Zhang, Minglong; Chong, Peter Han Joo

doi:10.1109/access.2023.3323022

Cited by 2 publications

(1 citation statement)

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“…This poses a significant challenge to deliver content within a limited period of time. The UE's transitions from one location to another location are modeled through Markov chain, and the UE's mobility pattern is random, therefore its connectivity follows Poisson distribution [20,21]. However, the UE's mobility can be utilized as an advantage because its mobility can increase the likelihood of discovering desired content within the D2D communication range.…”

Section: Introductionmentioning

confidence: 99%

Optimized Two-Tier Caching with Hybrid Millimeter-Wave and Microwave Communications for 6G Networks

Sheraz,

Chuah,

Roslee

et al. 2024

Applied Sciences

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Data caching is a promising technique to alleviate the data traffic burden from the backhaul and minimize data access delay. However, the cache capacity constraint poses a significant challenge to obtaining content through the cache resource that degrades the caching performance. In this paper, we propose a novel two-tier caching mechanism for data caching on mobile user equipment (UE) and the small base station (SBS) level in ultra-dense 6G heterogeneous networks for reducing data access failure via cache resources. The two-tier caching enables users to retrieve their desired content from cache resources through device-to-device (D2D) communications from neighboring users or the serving SBS. The cache-enabled UE exploits millimeter-wave (mmWave)-based D2D communications, utilizing line-of-sight (LoS) links for high-speed data transmission to content-demanding mobile UE within a limited connection time. In the event of D2D communication failures, a dual-mode hybrid system, combining mmWave and microwave μWave technologies, is utilized to ensure effective data transmission between the SBS and UE to fulfill users’ data demands. In the proposed framework. the data transmission speed is optimized through mmWave signals in line-of-sight (LoS) conditions. In non-LoS scenarios, the system switches to μWave mode for obstacle-penetrating signal transmission. Subsequently, we propose a reinforcement learning (RL) approach to optimize cache decisions through the approximation of the Q action-value function. The proposed technique undergoes iterative learning, adapting to dynamic network conditions to enhance the content placement policy and minimize delay. Extensive simulations demonstrate the efficiency of our proposed approach in significantly reducing network delay compared with benchmark schemes.

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Section: Introductionmentioning

confidence: 99%