Beamforming for small cell deployment in LTE-advanced and beyond

Bartoli, Giulio; Fantacci, R.; Letaief, Khaled B.; Marabissi, Dania; Privitera, Niccolo; Pucci, Marco; Zhang, Jun

doi:10.1109/mwc.2014.6812291

Cited by 54 publications

(23 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared with the SIR expression (7) in the perfect CSI case, it is clear that due to limited feedback, there is another part of interference, which is x∈Ψ (3) bĝ x x −α . Moreover,ĝ 0 no longer follows the gamma distribution with scale parameter 1, and its parameter will depend on the number of feedback bits B.…”

Section: A Limited Feedback Modelmentioning

confidence: 99%

See 1 more Smart Citation

User-Centric Intercell Interference Nulling for Downlink Small Cell Networks

Zhang

Haenggi

et al. 2015

IEEE Trans. Commun.

Self Cite

107

103

View full text Add to dashboard Cite

Small cell networks are regarded as a promising candidate to meet the exponential growth of mobile data traffic in cellular networks. With a dense deployment of access points, spatial reuse will be improved, and uniform coverage can be provided. However, such performance gains cannot be achieved without effective intercell interference management. In this paper, a novel interference coordination strategy, called user-centric intercell interference nulling, is proposed for small cell networks. A main merit of the proposed strategy is its ability to effectively identify and mitigate the dominant interference for each user. Different from existing works, each user selects the coordinating base stations (BSs) based on the relative distance between the home BS and the interfering BSs, called the interference nulling (IN) range, and thus interference nulling adapts to each user's own interference situation. By adopting a random spatial network model, we derive an approximate expression of the successful transmission probability to the typical user, which is then used to determine the optimal IN range. Simulation results shall confirm the tightness of the approximation, and demonstrate significant performance gains (about 35-40%) of the proposed coordination strategy, compared with the non-coordination case. Moreover, it is shown that the proposed strategy outperforms other interference nulling methods. Finally, the effect of imperfect channel state information (CSI) is investigated, where CSI is assumed to be obtained via limited feedback. It is shown that the proposed coordination strategy still provides significant performance gains even with a moderate number of feedback bits.Index Terms-Poisson point process, stochastic geometry, intercell interference nulling, small cell networks, limited feedback.

show abstract

Section: A Limited Feedback Modelmentioning

confidence: 99%

“…Recently, small cell networks have been proposed as a promising approach to address these challenges and further boost the network capacity. By deploying more access points, spatial reuse can be improved and more uniform coverage can be provided [2], [3].…”

Section: Introductionmentioning

confidence: 99%

User-Centric Intercell Interference Nulling for Downlink Small Cell Networks

Zhang

Haenggi

et al. 2015

IEEE Trans. Commun.

Self Cite

107

103

View full text Add to dashboard Cite

show abstract

“…(y ij log(r ij ) + (1 − y ij ) log(1 − r ij )). (5) Here N is the number of samples and K is the number of labels. y ij and r ij represent the true value and predicted value of the j-th label for the i-th sample, respectively.…”

Section: Neural-network-based Learning Approachmentioning

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

View full text Add to dashboard Cite

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.

show abstract

“…One of the most widely discussed proposals for systems beyond LTE (i.e., 5G) to overcome inter-cell interference is deployment of small cells in a separate higher frequency band [4]. The limited available spectrum clearly makes dedicated small cell deployment in the current cellular band highly unlikely.…”

Section: High Frequency Deploymentmentioning

confidence: 99%