961 research outputs found
Ubiquitous Cell-Free Massive MIMO Communications
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.Comment: Published in EURASIP Journal on Wireless Communications and
Networking on August 5, 201
Spectral Efficiency and Scalability Analysis for Multi-Level Cooperative Cell-Free Massive MIMO Systems
This paper proposes a multi-level cooperative architecture to balance the
spectral efficiency and scalability of cell-free massive multiple-input
multiple-output (MIMO) systems. In the proposed architecture, spatial expansion
units (SEUs) are introduced to avoid a large amount of computation at the
access points (APs) and increase the degree of cooperation among APs. We first
derive the closed-form expressions of the uplink user achievable rates under
multi-level cooperative architecture with maximal ratio combination (MRC) and
zero-forcing (ZF) receivers. The accuracy of the closed-form expressions is
verified. Moreover, numerical results have demonstrated that the proposed
multi-level cooperative architecture achieves a better trade-off between
spectral efficiency and scalability than other forms of cell-free massive MIMO
architectures.Comment: 5 pages, 3 figure
Soft Handover Procedures in mmWave Cell-Free Massive MIMO Networks
This paper considers a mmWave cell-free massive MIMO (multiple-input
multiple-output) network composed of a large number of geographically
distributed access points (APs) simultaneously serving multiple user equipments
(UEs) via coherent joint transmission. We address UE mobility in the downlink
(DL) with imperfect channel state information (CSI) and pilot training. Aiming
at extending traditional handover concepts to the challenging AP-UE association
strategies of cell-free networks, distributed algorithms for joint pilot
assignment and cluster formation are proposed in a dynamic environment
considering UE mobility. The algorithms provide a systematic procedure for
initial access and update of the serving APs and assigned pilot sequence to
each UE. The principal goal is to limit the necessary number of AP and pilot
changes, while limiting computational complexity. We evaluate the performance,
in terms of spectral efficiency (SE), with maximum ratio and regularized
zero-forcing precoding. Results show that our proposed distributed algorithms
effectively identify the essential AP-UE association refinements with
orders-of-magnitude lower computational time compared to the state-of-the-art.
It also provides a significantly lower average number of pilot changes compared
to an ultra-dense network (UDN). Moreover, we develop an improved pilot
assignment procedure that facilitates massive access to the network in highly
loaded scenarios.Comment: 14 pages, Accepted in IEEE Transactions on Wireless Communication
Local-Partial Signal Combining Schemes for Cell-Free Large-Scale MU-MIMO Systems with Limited Fronthaul Capacity and Spatial Correlation Channels
Cell-free large-scale multi-user MIMO is a promising technology for the 5G-and-beyond mobile communication networks. Scalable signal processing is the key challenge in achieving the benefits of cell-free systems. This study examines a distributed approach for cell-free deployment with user-centric configuration and finite fronthaul capacity. Moreover, the impact of scaling the pilot length, the number of access points (APs), and the number of antennas per AP on the achievable average spectral efficiency are investigated. Using the dynamic cooperative clustering (DCC) technique and large-scale fading decoding process, we derive an approximation of the signal-tointerference-plus-noise ratio in the criteria of two local combining schemes: Local-Partial Regularized Zero Forcing (RZF) and Local Maximum Ratio (MR). The results indicate that distributed approaches in the cell-free system have the advantage of decreasing the fronthaul signaling and the computing complexity. The results also show that the Local-Partial RZF provides the highest average spectral efficiency among all the distributed combining schemes because the computational complexity of the Local-Partial RZF is independent of the UTs. Therefore, it does not grow as the number of user terminals (UTs) increases
Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMO
Cell-free massive MIMO is emerging as a promising technology for future
wireless communication systems, which is expected to offer uniform coverage and
high spectral efficiency compared to classical cellular systems. We study in
this paper how cell-free massive MIMO can support federated edge learning.
Taking advantage of the additive nature of the wireless multiple access
channel, over-the-air computation is exploited, where the clients send their
local updates simultaneously over the same communication resource. This
approach, known as over-the-air federated learning (OTA-FL), is proven to
alleviate the communication overhead of federated learning over wireless
networks. Considering channel correlation and only imperfect channel state
information available at the central server, we propose a practical
implementation of OTA-FL over cell-free massive MIMO. The convergence of the
proposed implementation is studied analytically and experimentally, confirming
the benefits of cell-free massive MIMO for OTA-FL
Performance Analysis of Cell-Free Massive MIMO Systems: A Stochastic Geometry Approach
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Cell-free (CF) massive multiple-input-multiple-output (MIMO) has emerged as an alternative deployment for conventional cellular massive MIMO networks. As revealed by its name, this topology considers no cells, while a large number of multi-antenna access points (APs) serves simultaneously a smaller number of users over the same time/frequency resources through time-division duplex (TDD) operation. Prior works relied on the strong assumption (quite idealized) that the APs are uniformly distributed, and actually, this randomness was considered during the simulation and not in the analysis. However, in practice, ongoing and future networks become denser and increasingly irregular. Having this in mind, we consider that the AP locations are modeled by means of a Poisson point process (PPP) which is a more realistic model for the spatial randomness than a grid or uniform deployment. In particular, by virtue of stochastic geometry tools, we derive both the downlink coverage probability and achievable rate. Notably, this is the only work providing the coverage probability and shedding light on this aspect of CF massive MIMO systems. Focusing on the extraction of interesting insights, we consider small-cells (SCs) as a benchmark for comparison. Among the findings, CF massive MIMO systems achieve both higher coverage and rate with comparison to SCs due to the properties of favorable propagation, channel hardening, and interference suppression. Especially, we showed for both architectures that increasing the AP density results in a higher coverage which saturates after a certain value and increasing the number of users decreases the achievable rate but CF massive MIMO systems take advantage of the aforementioned properties, and thus, outperform SCs. In general, the performance gap between CF massive MIMO systems and SCs is enhanced by increasing the AP density. Another interesting observation concerns that a higher path-loss exponent decreases the rate while the users closer to the APs affect more the performance in terms of the rate.Peer reviewe
- …