Software-defined hyper-cellular architecture for green and elastic wireless access

Zhou, Sheng; Zhao, Tao; Niu, Zhisheng; Zhou, Shidong

doi:10.1109/mcom.2016.7378420

Cited by 54 publications

(18 citation statements)

References 15 publications

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“…A CBS with uniform linear array and half wavelength spacing is considered. Without lose of generality, a TBS with uniform linear array and half wavelength spacing is considered 3 . 20000 UEs are randomly placed under the coverage of the TBS following uniform distribution.…”

Section: A Simulation Settingsmentioning

confidence: 99%

“…For each sample, the performance metric is the received signal strength using selected beam pattern divided by that using optimal beam pattern from the codebook. We use this as our performance metric along with prediction accuracy because there is a chance that UEs have almost the same received signal strength 3 Interference from other TBSs will be considered in future work. using two beamforming vectors, in which case the optimal beamforming pattern can be either one of them.…”

Section: A Simulation Settingsmentioning

confidence: 99%

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Remote Channel Inference for Beamforming in Ultra-Dense Hyper-Cellular Network

Chen

Jiang

Liu

et al. 2017

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

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In this paper, we propose a learning-based lowoverhead channel estimation method for coordinated beamforming in ultra-dense networks. We first show through simulation that the channel state information (CSI) of geographically separated base stations (BSs) exhibits strong non-linear correlations in terms of mutual information. This finding enables us to adopt a novel learning-based approach to remotely infer the quality of different beamforming patterns at a dense-layer BS based on the CSI of an umbrella control-layer BS. The proposed scheme can reduce channel acquisition overhead by replacing pilot-aided channel estimation with the online inference from an artificial neural network, which is fitted offline. Moreover, we propose to exploit joint learning of multiple CBSs and involve more candidate beam patterns to obtain better performance. Simulation results based on stochastic ray-tracing channel models show that the proposed scheme can reach an accuracy of 99.74% in settings with 20 beamforming patterns.

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Section: A Simulation Settingsmentioning

confidence: 99%

Section: A Simulation Settingsmentioning

confidence: 99%

Remote Channel Inference for Beamforming in Ultra-Dense Hyper-Cellular Network

Chen

Jiang

Liu

et al. 2017

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

Self Cite

View full text Add to dashboard Cite

show abstract

“…As the definition of EE [15,16], in order to investigate the EE, achievable sum rate and consumed energy expression should be achieved beforehand. Thus in the following subsections, the optimized system model, the sum rate, and its consumed energy investigation based on this model were addressed beforehand.…”

Section: Proposed System Model and Analysis Of The Transmission Rate mentioning

confidence: 99%

Integrating energy efficiency analysis of massive MIMO-based C-RAN

Zhang

Tariq

Mumtaz

et al. 2016

J Wireless Com Network

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Besides the prior study of energy efficiency (EE) that focuses on single component (antenna, radio frequency (RF) chain, circuit, etc.), or limited to one base station (BS) area, in this paper, EE is integrated, investigated with various components (antenna, RF chain, circuit, coverage area) considering in, based on an optimized system model. That is, one optimized multi-BS system model is introduced beforehand with the cloud radio access network (C-RAN) for easier deployment of the selecting/sleeping mechanism. In addition, the system EE is described by an optimization problem while taking more component factors into consideration. While solving, this integrated optimization problem in all time intervals of all BS areas is proved to be equal to an optimization problem of each single BS area within each single time interval. After that, the integrated optimization solution is obtained while adding up the single-BS-singleinterval optimization solutions, whereas the later solution is addressed by an offline decision method plus a convex optimization strategy. Observation from the numerical results demonstrates that the antenna power consumption is the dominant factor of the power consumption, which therefore, the dominant factor of EE performance.

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“…Centralized processing is combined with dynamical fronthaul switching to address the mobility and energy efficiency issues of small cells in [8], [9]. Concerning realization-related issues, it is demonstrated in [10]- [14] that BBU functionalities can be implemented as software, i.e. virtual base station (VBS), which runs on general purpose platforms (GPP).…”

Section: Introductionmentioning

confidence: 99%

Statistical Multiplexing Gain Analysis of Heterogeneous Virtual Base Station Pools in Cloud Radio Access Networks

Liu

Zhou

Gong

et al. 2016

IEEE Trans. Wireless Commun.

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Abstract-Cloud radio access network (C-RAN) is proposed recently to reduce network cost, enable cooperative communications, and increase system flexibility through centralized baseband processing. By pooling multiple virtual base stations (VBSs) and consolidating their stochastic computational tasks, the overall computational resource can be reduced, achieving the so-called statistical multiplexing gain. In this paper, we evaluate the statistical multiplexing gain of VBS pools using a multi-dimensional Markov model, which captures the session-level dynamics and the constraints imposed by both radio and computational resources. Based on this model, we derive a recursive formula for the blocking probability and also a closed-form approximation for it in large pools. These formulas are then used to derive the session-level statistical multiplexing gain of both real-time and delay-tolerant traffic. Numerical results show that VBS pools can achieve more than 75% of the maximum pooling gain with 50 VBSs, but further convergence to the upper bound (large-pool limit) is slow because of the quickly diminishing marginal pooling gain, which is inversely proportional to a factor between the onehalf and three-fourth power of the pool size. We also find that the pooling gain is more evident under light traffic load and stringent Quality of Service (QoS) requirement.

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Software-defined hyper-cellular architecture for green and elastic wireless access

Cited by 54 publications

References 15 publications

Remote Channel Inference for Beamforming in Ultra-Dense Hyper-Cellular Network

Remote Channel Inference for Beamforming in Ultra-Dense Hyper-Cellular Network

Integrating energy efficiency analysis of massive MIMO-based C-RAN

Statistical Multiplexing Gain Analysis of Heterogeneous Virtual Base Station Pools in Cloud Radio Access Networks

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