Clustered Cell-Free Massive MIMO

Riera-Palou, Felip; Femenias, Guillem; Armada, Ana García; Pérez-Neira, Ana I.

doi:10.1109/glocomw.2018.8644255

Cited by 46 publications

(34 citation statements)

References 14 publications

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“…The above levels have been partially analyzed before in the literature, but not under the general and practical conditions considered in this paper, which allow us to draw conclusions that differ in several important ways-in particular, we show that MR combining performs terribly bad in Cell-free mMIMO. Level 4 was considered in [24], [30], [31] for N = 1 and in [27], [32] with N ≥ 1 but with spatially uncorrelated channels. Level 3 was investigated in [24], [33] for N = 1 and MR combining.…”

Section: B Contributionsmentioning

confidence: 99%

Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation

Björnson

Sanguinetti

2020

IEEE Trans. Wireless Commun.

571

593

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Cell-free Massive MIMO is considered as a promising technology for satisfying the increasing number of users and high rate expectations in beyond-5G networks. The key idea is to let many distributed access points (APs) communicate with all users in the network, possibly by using joint coherent signal processing. The aim of this paper is to provide the first comprehensive analysis of this technology under different degrees of cooperation among the APs. Particularly, the uplink spectral efficiencies of four different cell-free implementations are analyzed, with spatially correlated fading and arbitrary linear processing. It turns out that it is possible to outperform conventional Cellular Massive MIMO and small cell networks by a wide margin, but only using global or local minimum mean-square error (MMSE) combining. This is in sharp contrast to the existing literature, which advocates for maximum-ratio combining. Also, we show that a centralized implementation with optimal MMSE processing not only maximizes the SE but largely reduces the fronthaul signaling compared to the standard distributed approach. This makes it the preferred way to operate Cell-free Massive MIMO networks. Non-linear decoding is also investigated and shown to bring negligible improvements. ). same time-frequency resource [8]. The characteristic feature of mMIMO, compared to traditional multi-user MIMO, is that each BS has many more antennas than UEs in the cell. Signal processing methods, such as minimum mean-squared error (MMSE) combining in the uplink, can be used individually at each BS to suppress interference from both the same and other cells [3], [9], [10], without the need for any BS cooperation. The mMIMO theory also supports deployments with spatially distributed arrays in each cell [11], [12], as also illustrated in Fig. 1(a). This setup is essentially the same as the Distributed Antenna System (DAS) setup in [13] and Coordinated Multi-Point (CoMP) with static, disjoint cooperation clusters [14], [15]. These are all different embodiments of cellular networks.An alternative network infrastructure was considered in [16], [17] under the name of Cell-free mMIMO. The idea is to deploy a large number of distributed single-antenna access points (APs), which are connected to a central processing unit (CPU), also known as an edge-cloud processor [18] or C-RAN (cloud radio access network) data center [19]. The CPU operates the system in a Network MIMO fashion, with no cell boundaries, to jointly serve the UEs by coherent joint transmission and reception [15], [20]-[23]. Compared to traditional Network MIMO, the outstanding aspect of Cellfree mMIMO is the operating regime with many more APs than UEs [16]. From an analytical perspective, an important novelty was that imperfect channel state information (CSI) was considered in the performance analysis, while perfect CSI was often assumed in the past [15]. The paper [16] advocated the use of maximum ratio (MR) processing (a.k.a. matched filtering or conjugate beamforming) locally at each AP, whil...

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Section: B Contributionsmentioning

confidence: 99%

Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation

Björnson

Sanguinetti

2020

IEEE Trans. Wireless Commun.

571

593

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“…. β Mk ] into M A clusters in which each propagation vector belongs to the cluster with the nearest centroid, serving as a representative of the cluster [48]. Now, using the virtual propagation losses vectors characterizing the M A centroids, the same selection procedure previously described is applied to select the M A active APs.…”

Section: E Propagation Losses-aware Asomentioning

confidence: 99%

Access Point Switch ON/OFF Strategies for Green Cell-Free Massive MIMO Networking

2020

Self Cite

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The combination of user-centric network densification and distributed massive multiple-input multiple-output (MIMO) operation has recently brought along a new paradigm in the wireless communications arena, referred to as cell-free massive MIMO networking. In these networks, a large number of distributed access points (APs), coordinated by a central processing unit (CPU), cooperate to coherently serve a large number of mobile stations (MSs) in the same time/frequency resource. Similar to what has been traditionally done with conventional cellular networks, cell-free massive MIMO networks will be dimensioned to provide the required quality of service (QoS) to MSs under heavy traffic load conditions, and thus they might be underutilized during low traffic load periods, leading to an inefficient use of both spectral and energy resources. Aiming at the implementation of green cell-free massive MIMO networks, this paper proposes and analyzes the performance of different AP switch ON/OFF (ASO) strategies designed to dynamically turn ON/OFF some of the APs based on the number and/or location of the active MSs in the network. The proposed framework considers line-of-sight (LOS) and non-line-of-sight (NLOS) links between APs and MSs, the use of different antenna array architectures at the access points (APs), suitably characterized by array-dependent spatial correlation matrices, and specific power consumption models for APs, MSs and fronthaul links between the APs and the CPU. Numerical results show that the use of properly designed ASO strategies in cell-free massive MIMO networks clearly improve the achievable energy efficiency. Moreover, they also reveal the existing trade-offs among the achievable energy efficiency, the available network-state information, and the hardware configuration (i.e., number of APs, number of transmit antennas per AP, and number of MSs). INDEX TERMS AP ON/OFF switching, green networking, cell-free massive MIMO, zero-forcing precoding.

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“…In particular, dynamic clustering should take into account the underlying pre-determined clusters that are established by the physical links, while signal processing and power control should be able to smoothly adapt to the dynamic clustering (Paper B). A -means clustering algorithm is proposed in [137] to minimize the pilot contamination within each cluster. [138] devises a clusterization based on the degree of channel hardening, while [116,139] proposes AP selection methods based on the large-scale fading coefficients.…”

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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Clustered Cell-Free Massive MIMO

Cited by 46 publications

References 14 publications

Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation

Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation

Access Point Switch ON/OFF Strategies for Green Cell-Free Massive MIMO Networking

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

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