Antenna Selection for uplink CoMP in dense small-cell clusters

Zeineddine, Khalid; Honig, Michael L.; Nagaraj, S.; Fleming, Philip J.

doi:10.1109/spawc.2013.6612016

Cited by 6 publications

(1 citation statement)

References 13 publications

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“…In [14], the authors proposed resource allocation method for CoMP transmissions in LTE based ultra-dense SCN. In [15] and [16], the authors proposed antenna selection methods to implement CoMP in ultra-dense SCN, where the SBSs are deployed in pre-defined forms, such as lining up in the same line or in hexagonal form with equal spacing. In [17], the authors studied the coordinated communication schemes for dense SCN and proposed CoMP set formation method for it.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Signal Detection for CoMP Downlink in Ultra-Dense Small Cell Networks

et al. 2020

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In the next generation wireless communication systems, high-speed data rate, reliable link quality, and ubiquitous access are necessary requirement. To meet the next generation communication system requirements, the ultra-dense small cell network (SCN) is proposed as one of the possible candidate solutions. To achieve high data rate and reliable link quality, coordinated multiple point (CoMP) transmission is usually used in ultra-dense SCN to satisfy the performance target. However, to realize CoMP transmissions in ultradense SCN, the feedback load is heavy because of the large amount of small cells and users. In this study, we want to investigate the ultra-dense SCN environment and design an effective method for its downlink (DL) transmission. This method consists of iterative scheme which iteratively solves the received signal with proper step-size value γ. The step-size value γ is very important to the convergence performance of iterative scheme because it affects the convergence speed and the converged error level of the iterative algorithm. Therefore, in this paper, we propose a novel machine learning based method which creates data model from input data. When the model is well established, the optimal step-size can be well estimated and the feedback information can become rough and the bandwidth allocated for feedback can be saved for data transmission. The simulations show that, the proposed method can create good model and achieve better convergence performance. For example, about the distribution of the transmissions with convergence iteration number less than 10 4 level, the proposed method can obtain 30% improvement than the traditional method with fixed step-size γ = 0.01. INDEX TERMS Small cell network (SCN), ultra-dense SCN, coordinated multi-point (CoMP) transmission, feedback reduction, machine learning, neural network.

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Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Signal Detection for CoMP Downlink in Ultra-Dense Small Cell Networks

et al. 2020

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An energy-saving mechanism for mobile terminals based on LTE-A uplink CoMP

Wen

Feng

et al. 2014

The 16th Asia-Pacific Network Operations and Management Symposium

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Distributed optimization of multi-cell uplink co-operation with backhaul constraints

Nagaraj

Honig

Zeineddine

2015

2015 IEEE International Conference on Communications (ICC)

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Abstract-We address the problem of uplink co-operative reception with constraints on both backhaul bandwidth and the receiver aperture, or number of antenna signals that can be processed. The problem is cast as a network utility (weighted sum rate) maximization subject to computational complexity and architectural bandwidth sharing constraints. We show that a relaxed version of the problem is convex, and can be solved via a dual-decomposition. The proposed solution is distributed in that each cell broadcasts a set of demand prices based on the data sharing requests they receive. Given the demand prices, the algorithm determines an antenna/cell ordering and antennaselection for each scheduled user in a cell. This algorithm, referred to as LiquidMAAS, iterates between the preceding two steps. Simulations of realistic network scenarios show that the algorithm exhibits fast convergence even for systems with large number of cells.

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Antenna Selection for uplink CoMP in dense small-cell clusters

Cited by 6 publications

References 13 publications

Machine Learning-Based Signal Detection for CoMP Downlink in Ultra-Dense Small Cell Networks

Machine Learning-Based Signal Detection for CoMP Downlink in Ultra-Dense Small Cell Networks

An energy-saving mechanism for mobile terminals based on LTE-A uplink CoMP

Distributed optimization of multi-cell uplink co-operation with backhaul constraints

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