Pilot Allocation and Sum-Rate Analysis in Cell-Free Massive MIMO Systems

Sabbagh, Ramiz; Pan, Cunhua; Wang, Jiangzhou

doi:10.1109/icc.2018.8422575

Cited by 29 publications

(18 citation statements)

References 19 publications

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“…Then this assignment is iteratively improved by performing small changes that increase the utility. • Structured/Clustering pilot assignment [35,36]:…”

Section: Uplink Pilot Assignmentmentioning

confidence: 99%

Ubiquitous cell-free Massive MIMO communications

Interdonato

Björnson

Ngo

et al. 2019

J Wireless Com Network

514

400

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Since the first cellular networks were trialled in the 1970s, we have witnessed an incredible wireless revolution. From 1G to 4G, the massive traffic growth has been managed by a combination of wider bandwidths, refined radio interfaces, and network densification, namely increasing the number of antennas per site. Due its cost-efficiency, the latter has contributed the most. Massive MIMO (multiple-input multiple-output) is a key 5G technology that uses massive antenna arrays to provide a very high beamforming gain and spatially multiplexing of users, and hence, increases the spectral and energy efficiency. It constitutes a centralized solution to densify a network, and its performance is limited by the inter-cell interference inherent in its cell-centric design. Conversely, ubiquitous cell-free Massive MIMO refers to a distributed Massive MIMO system implementing coherent user-centric transmission to overcome the inter-cell interference limitation in cellular networks and provide additional macro-diversity. These features, combined with the system scalability inherent in the Massive MIMO design, distinguishes ubiquitous cell-free Massive MIMO from prior coordinated distributed wireless systems. In this article, we investigate the enormous potential of this promising technology while addressing practical deployment issues to deal with the increased back/front-hauling overhead deriving from the signal co-processing.

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“…Then this assignment is iteratively improved by performing small changes that increase the utility. • Structured/Clustering pilot assignment [35,36]:…”

Section: Uplink Pilot Assignmentmentioning

confidence: 99%

Ubiquitous cell-free Massive MIMO communications

Interdonato

Björnson

Ngo

et al. 2019

J Wireless Com Network

514

400

View full text Add to dashboard Cite

show abstract

“…Literature [3] introduced the channel mathematical model and signal transmission model of Cell-Free Massive MIMO system, in which the LoS transmission model is used to represent the channel model. In literature [4], a pilot assignment and channel estimation method in Cell-Free Massive MIMO uplink training is studied. The authors applied a MMSE channel estimation algorithm which is wide used in this research field.…”

Section: A Related Workmentioning

confidence: 99%

“…In this paper, refer to literature [4], we divide the channel estimation operation into two stages. In the first stage, all users simultaneously send their pilot sequence 1 ,...,      C to APs, and the signal received by the th m  AP can be expressed as:…”

Section: A Channel Estimationmentioning

confidence: 99%

Trajectory Optimization and Power Allocation Algorithm in MBS-Assisted Cell-Free Massive MIMO Systems

Zhao

2021

IEEE Access

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Cell-Free Massive multiple-input multiple-output (MIMO) system contains a large number of access points (APs) and users that are close together. The APs and users are clustered so that each user can receive services from only a part of APs, thus providing high system capacity. But when the number of users and traffic demand increase, sometimes it is difficult to meet the need of users. This paper designs a new heterogeneous network system: Use a unmanned aerial vehicle (UAV) as the mobile base station (MBS) to form an air-ground heterogeneous system with the Cell-Free Massive MIMO system and offload part of traffic to the MBS to further improve the system performance. We design a flight trajectory optimization and power allocation algorithm based on the consideration of the fairness of service quality, so that the flight trajectory of the MBS should have a high user coverage, then we joint optimize the power allocation of Cell-Free Massive MIMO and MBS to maximize the average minimum user rate during the flight period of the MBS. It can be seen from the simulation results that the heterogeneous network designed in this paper has a significant improvement in system performance compared with the normal Cell-Free Massive MIMO system.

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“…A greedy algorithm for pilot assignment maximizing the minimum rate in 4 Scope and Related Works the network is devised in [58]. While, the pilot reuse schemes in [140,141] reduce the pilot contamination by ensuring that the co-pilot users are enough spatially separated. A pilot allocation policy based on the tabu search, the Hungarian algorithm, and the graph coloring are presented in [142][143][144], respectively.…”

Section: Scalability: a Framework For Pilot Assignmentmentioning

confidence: 99%

Cell-Free Massive MIMO : Scalability, Signal Processing and Power Control

Interdonato

2020

Linköping Studies in Science and Technology. Dissertations

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Linköping 2020 This is a Swedish Doctor of Philosophy thesis. The Doctor of Philosophy degree comprises 240 ECTS credits of postgraduate studies. Cover: Illustration of the Ericsson Radio Stripes concept, co-invented by the author of this thesis. The Radio Stripes constitute a way to implement a cell-free massive MIMO system. They aim at enabling invisible, low-cost deployment of large number of distributed access points in the vicinity of the users. This is achieved by serial integration of several transmitting and receiving components into a cable (or stripe) which also provides fronthaul communication and power supply.

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Pilot Allocation and Sum-Rate Analysis in Cell-Free Massive MIMO Systems

Abstract: The version in the Kent Academic Repository may differ from the final published version. Users are advised to check http://kar.kent.ac.uk for the status of the paper. Users should always cite the published version of record.

Cited by 29 publications

References 19 publications

Ubiquitous cell-free Massive MIMO communications

Ubiquitous cell-free Massive MIMO communications

Trajectory Optimization and Power Allocation Algorithm in MBS-Assisted Cell-Free Massive MIMO Systems

Cell-Free Massive MIMO : Scalability, Signal Processing and Power Control

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