Hardware and Spectrum Sharing for Distributed Massive MIMO

Guevara, Andrea P.; Chen, Cheng-Ming; Pollin, Sofie

doi:10.1109/acssc.2018.8645516

Cited by 6 publications

(8 citation statements)

References 13 publications

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“…The available KU Leuven massive MIMO BS from ESAT, described in [15], contains 64 antennas in two rectangular arrays. Table II summarises the most important parameters applied to the testbed during the aforementioned experiment.…”

Section: A Testbed Components and Data Collectionmentioning

confidence: 99%

Matrix Pencil Method: Angle of Arrival and Channel Estimation for a Massive MIMO system

Monteyne

Guevara

Callebaut

et al. 2020

ICC 2020 - 2020 IEEE International Conference on Communications (ICC)

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Channel estimation is essential in massive MIMO systems. Pilot Contamination (PC) however, causes a major bottleneck in the acquisition of this information. The exploitation of the Angle of Arrival (AoA) provides multiple techniques for channel estimation under PC. However, many AoA estimation techniques require information on the signal statistics which is not available in dynamic scenarios. In this paper we propose and analyse the Matrix Pencil Method (MPM) to decorrelate contaminated channels based on their estimated AoA. We evaluate this method both through simulations and experiments in a real-life testbed. Our assessment focuses on a system with a Uniform Linear Array (ULA). The performance of the MPM is validated through simulations 1 with varying number of antennas, SNR and AoA difference. The results show that our approach effectively decorrelates the channels starting from 20 antennas and an SNR of 15 dB, which outperforms the theoretical expectation. This allows us to enhance the channel estimation quality under PC to the level of no PC. Real-life measurements confirm the simulated results. Our MPM implementation can achieve a target AoA estimation accuracy both with and without PC. We anticipate that the method can be extended for a Uniform Rectangular Array (URA).

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Section: A Testbed Components and Data Collectionmentioning

confidence: 99%

Matrix Pencil Method: Angle of Arrival and Channel Estimation for a Massive MIMO system

Monteyne

Guevara

Callebaut

et al. 2020

ICC 2020 - 2020 IEEE International Conference on Communications (ICC)

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“…The benefits of a distributed array deployment in terms of spectral efficiency have also been confirmed experimentally [12]. The recently proposed "radio stripes" present a distributed array architecture to address capacity and coverage challenges, leveraging on a one-dimensional sequential architecture [13].…”

Section: A Distributed Antenna Elements and Arrays: Concept And Valumentioning

confidence: 99%

RadioWeaves for efficient connectivity: analysis and impact of constraints in actual deployments

Perre

Larsson

Tufvesson

et al. 2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

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We present a new type of wireless access infrastructure consisting of a fabric of dispersed electronic circuits and antennas that collectively function as a massive, distributed antenna array. We have chosen to name this new wireless infrastructure "RadioWeaves" and anticipate they can be integrated into indoor and outdoor walls, furniture, and other objects, rendering them a natural part of the environment. Technologically, RadioWeaves will deploy distributed arrays to create both favorable propagation and antenna array interaction. The technology leverages on the ideas of large-scale intelligent surfaces and cell-free wireless access. Offering close to the service connectivity and computing, new grades in energy efficiency, reliability, and low latency can be reached. The new concept moreover can be scaled up easily to offer a very high capacity in specific areas demanding so. In this paper we anticipate how two different demanding use cases can be served well by a dedicated RadioWeaves deployment: a crowd scenario and a highly reflective factory environment. A practical approach towards a RadioWeaves prototype, integrating dispersed electronics invisibly in a room environment, is introduced. We outline the many and diverse R&D challenges that need to be addressed to realize the great potential of the RadioWeaves technology. Index Terms-RadioWaves, energy efficient and ultra reliable connectivity, large-scale antenna arrays, cell-free wireless access. L. Van der Perre and L. De Strycker are with the Department of Electrical Engineering, KU Leuven, Belgium; L. Van der Perre, F. Tufvesson, and O. Edfors are with the

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“…Once pilot decorrelation is carried out we can estimate each UE's channel. In this work the MMSE channel estimation based Theorem 3.1 from [4] is used as follows: h j jk = p jk R j jk Ψ jk y j jk (5) where…”

Section: B Mmse Channel Estimationmentioning

confidence: 99%

“…The distributed KULeuven massive MIMO testbed used in an outdoor two-cell experiment is described in detail in [5]. This testbed is supported by National Instrument universal software radio peripherals (USRPs) and controlled in real time by the LabVIEW Communications MIMO Application Framework 1.1.…”

Section: A Experiments Setupmentioning

confidence: 99%

Partial Multi-Cell MMSE Vector Combining to Reduce Computational Cost for Massive MIMO Systems

Guevara

Chen

Pollin

2019

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

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A practical partial multi-cell MMSE (PM-MMSE) combining vector for multi-cell massive MIMO systems is proposed in this work. This new scheme uses only inter-cell channels causing strong interference as partial inter-cell information in the multi-cell MMSE approach. The performance of PM-MMSE is evaluated using channels measured in a twocell outdoor experiment. In addition, a SIR threshold Γ is introduced as a tradeoff parameter between spectral efficiency and computational cost. For multi-cell scenarios, simulation results show that in a system with 16 cells and 320 users, PM-MMSE is capable of achieving 96% of the M-MMSE spectral efficiency using on average a tenth of inter-cell channel information. For this scenario, PM-MMSE requires only 60% of the total number of multiplications used by M-MMSE.

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Hardware and Spectrum Sharing for Distributed Massive MIMO

Cited by 6 publications

References 13 publications

Matrix Pencil Method: Angle of Arrival and Channel Estimation for a Massive MIMO system

Matrix Pencil Method: Angle of Arrival and Channel Estimation for a Massive MIMO system

RadioWeaves for efficient connectivity: analysis and impact of constraints in actual deployments

Partial Multi-Cell MMSE Vector Combining to Reduce Computational Cost for Massive MIMO Systems

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