721 research outputs found
A New Cell Association Scheme In Heterogeneous Networks
Cell association scheme determines which base station (BS) and mobile user
(MU) should be associated with and also plays a significant role in determining
the average data rate a MU can achieve in heterogeneous networks. However, the
explosion of digital devices and the scarcity of spectra collectively force us
to carefully re-design cell association scheme which was kind of taken for
granted before. To address this, we develop a new cell association scheme in
heterogeneous networks based on joint consideration of the
signal-to-interference-plus-noise ratio (SINR) which a MU experiences and the
traffic load of candidate BSs1. MUs and BSs in each tier are modeled as several
independent Poisson point processes (PPPs) and all channels experience
independently and identically distributed ( i.i.d.) Rayleigh fading. Data rate
ratio and traffic load ratio distributions are derived to obtain the tier
association probability and the average ergodic MU data rate. Through numerical
results, We find that our proposed cell association scheme outperforms cell
range expansion (CRE) association scheme. Moreover, results indicate that
allocating small sized and high-density BSs will improve spectral efficiency if
using our proposed cell association scheme in heterogeneous networks.Comment: Accepted by IEEE ICC 2015 - Next Generation Networking Symposiu
Spatial spectrum and energy efficiency of random cellular networks
It is a great challenge to evaluate the network performance of cellular
mobile communication systems. In this paper, we propose new spatial spectrum
and energy efficiency models for Poisson-Voronoi tessellation (PVT) random
cellular networks. To evaluate the user access the network, a Markov chain
based wireless channel access model is first proposed for PVT random cellular
networks. On that basis, the outage probability and blocking probability of PVT
random cellular networks are derived, which can be computed numerically.
Furthermore, taking into account the call arrival rate, the path loss exponent
and the base station (BS) density in random cellular networks, spatial spectrum
and energy efficiency models are proposed and analyzed for PVT random cellular
networks. Numerical simulations are conducted to evaluate the network spectrum
and energy efficiency in PVT random cellular networks.Comment: appears in IEEE Transactions on Communications, April, 201
Deep Learning-Based Modeling of 5G Core Control Plane for 5G Network Digital Twin
Digital twin is a key enabler to facilitate the development and
implementation of new technologies in 5G and beyond networks. However, the
complex structure and diverse functions of the current 5G core network,
especially the control plane, lead to difficulties in building the core network
of the digital twin. In this paper, we propose two novel data-driven
architectures for modeling the 5G control plane and implement corresponding
deep learning models, namely 5GC-Seq2Seq and 5GC-former, based on the Vanilla
Seq2Seq model and Transformer decoder respectively. To train and test models,
we also present a solution that allows the signaling messages to be
interconverted with vectors, which can be utilized in dataset construction. The
experiments are based on 5G core network signaling data collected by the
Spirent C50 network tester, including various procedures related to
registration, handover, PDU sessions, etc. Our results show that 5GC-Seq2Seq
achieves over 99.98% F1-score (A metric to measure the accuracy of positive
samples) with a relatively simple structure, while 5GC-former attains higher
than 99.998% F1-score by establishing a more complex and highly parallel model,
indicating that the method proposed in this paper reproduces the major
functions of the core network control plane in 5G digital twin with high
accuracy
Wireless Network Digital Twin for 6G: Generative AI as A Key Enabler
Digital twin, which enables emulation, evaluation, and optimization of
physical entities through synchronized digital replicas, has gained
increasingly attention as a promising technology for intricate wireless
networks. For 6G, numerous innovative wireless technologies and network
architectures have posed new challenges in establishing wireless network
digital twins. To tackle these challenges, artificial intelligence (AI),
particularly the flourishing generative AI, emerges as a potential solution. In
this article, we discuss emerging prerequisites for wireless network digital
twins considering the complicated network architecture, tremendous network
scale, extensive coverage, and diversified application scenarios in the 6G era.
We further explore the applications of generative AI, such as transformer and
diffusion model, to empower the 6G digital twin from multiple perspectives
including implementation, physical-digital synchronization, and slicing
capability. Subsequently, we propose a hierarchical generative AI-enabled
wireless network digital twin at both the message-level and policy-level, and
provide a typical use case with numerical results to validate the effectiveness
and efficiency. Finally, open research issues for wireless network digital
twins in the 6G era are discussed
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