34 research outputs found
Joint Optimization of Uplink Power and Computational Resources in Mobile Edge Computing-Enabled Cell-Free Massive MIMO
The coupling of cell-free massive MIMO (CF-mMIMO) with Mobile Edge Computing
(MEC) is investigated in this paper. A MEC-enabled CF-mMIMO architecture
implementing a distributed user-centric approach both from the radio and the
computational resource allocation perspective is proposed. An optimization
problem for the joint allocation of uplink powers and remote computational
resources is formulated, aimed at striking an optimal balance between the total
uplink power consumption and the sum SE throughout the network, under power
budget and latency constraints. In order to efficiently solve such a
challenging non-convex problem, an iterative algorithm based on sequential
convex programming is proposed, along with two approaches to priory assess the
problem feasibility. Finally, a detailed performance comparison between the
proposed MEC-enabled user-centric CF-mMIMO architecture and its network-centric
(both centralized and distributed) counterpart, is provided. Numerical results
reveal the effectiveness of the proposed joint optimization problem, under
different AP selection strategies, and the natural suitability of CF-mMIMO in
supporting computation-offloading applications with benefits over users'
transmit power and energy consumption, the offloading latency experienced, and
the total amount of allocated remote computational resources.Comment: This paper has been submitted for publication in an IEEE journal.
{\copyright} 2022 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other use
Downlink Spectral Efficiency of Cell-Free Massive MIMO with Full-Pilot Zero-Forcing
Cell-free Massive multiple-input multiple-output (MIMO) ensures ubiquitous
communication at high spectral efficiency (SE) thanks to increased
macro-diversity as compared cellular communications. However, system
scalability and performance are limited by fronthauling traffic and
interference. Unlike conventional precoding schemes that only suppress
intra-cell interference, full-pilot zero-forcing (fpZF), introduced in [1],
actively suppresses also inter-cell interference, without sharing channel state
information (CSI) among the access points (APs). In this study, we derive a new
closed-form expression for the downlink (DL) SE of a cell-free Massive MIMO
system with multi-antenna APs and fpZF precoding, under imperfect CSI and pilot
contamination. The analysis also includes max-min fairness DL power
optimization. Numerical results show that fpZF significantly outperforms
maximum ratio transmission scheme, without increasing the fronthauling
overhead, as long as the system is sufficiently distributed.Comment: Paper published in 2018 IEEE Global Conference on Signal and
Information Processing (GlobalSIP). {\copyright} 2019 IEEE. Personal use of
this material is permitted. Permission from IEEE must be obtained for all
other use
Downlink Training in Cell-Free Massive MIMO: A Blessing in Disguise
Cell-free Massive MIMO (multiple-input multiple-output) refers to a
distributed Massive MIMO system where all the access points (APs) cooperate to
coherently serve all the user equipments (UEs), suppress inter-cell
interference and mitigate the multiuser interference. Recent works demonstrated
that, unlike co-located Massive MIMO, the \textit{channel hardening} is, in
general, less pronounced in cell-free Massive MIMO, thus there is much to
benefit from estimating the downlink channel. In this study, we investigate the
gain introduced by the downlink beamforming training, extending the previously
proposed analysis to non-orthogonal uplink and downlink pilots. Assuming
single-antenna APs, conjugate beamforming and independent Rayleigh fading
channel, we derive a closed-form expression for the per-user achievable
downlink rate that addresses channel estimation errors and pilot contamination
both at the AP and UE side. The performance evaluation includes max-min
fairness power control, greedy pilot assignment methods, and a comparison
between achievable rates obtained from different capacity-bounding techniques.
Numerical results show that downlink beamforming training, although increases
pilot overhead and introduces additional pilot contamination, improves
significantly the achievable downlink rate. Even for large number of APs, it is
not fully efficient for the UE relying on the statistical channel state
information for data decoding.Comment: Published in IEEE Transactions on Wireless Communications on August
14, 2019. {\copyright} 2019 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other use
How Much Do Downlink Pilots Improve Cell-Free Massive MIMO?
In this paper, we analyze the benefits of including downlink pilots in a
cell-free massive MIMO system. We derive an approximate per-user achievable
downlink rate for conjugate beamforming processing, which takes into account
both uplink and downlink channel estimation errors, and power control. A
performance comparison is carried out, in terms of per-user net throughput,
considering cell-free massive MIMO operation with and without downlink
training, for different network densities. We take also into account the
performance improvement provided by max-min fairness power control in the
downlink. Numerical results show that, exploiting downlink pilots, the
performance can be considerably improved in low density networks over the
conventional scheme where the users rely on statistical channel knowledge only.
In high density networks, performance improvements are moderate.Comment: 7 pages, 5 figures. IEEE Global Communications Conference 2016
(GLOBECOM). Accepte
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
Self-Learning Detector for the Cell-Free Massive MIMO Uplink: The Line-of-Sight Case
The precoding in cell-free massive multiple-input multiple-output (MIMO)
technology relies on accurate knowledge of channel responses between users
(UEs) and access points (APs). Obtaining high-quality channel estimates in turn
requires the path losses between pairs of UEs and APs to be known. These path
losses may change rapidly especially in line-of-sight environments with moving
blocking objects. A difficulty in the estimation of path losses is pilot
contamination, that is, simultaneously transmitted pilots from different UEs
that may add up destructively or constructively by chance, seriously affecting
the estimation quality (and hence the eventual performance). A method for
estimation of path losses, along with an accompanying pilot transmission
scheme, is proposed that works for both Rayleigh fading and line-of-sight
channels and that significantly improves performance over baseline
state-of-the-art. The salient feature of the pilot transmission scheme is that
pilots are structurally phase-rotated over different coherence blocks
(according to a pre-determined function known to all parties), in order to
create an effective statistical distribution of the received pilot signal that
can be efficiently exploited by the proposed estimation algorithm.Comment: Paper accepted for presentation in IEEE SPAWC 2020 - 21st IEEE
International Workshop on Signal Processing Advances in Wireless
Communications. {\copyright} 2020 IEEE. Personal use of this material is
permitted. Permission from IEEE must be obtained for all other use
Local Partial Zero-Forcing Precoding for Cell-Free Massive MIMO
Cell-free Massive MIMO (multiple-input multiple-output) is a promising
distributed network architecture for 5G-and-beyond systems. It guarantees
ubiquitous coverage at high spectral efficiency (SE) by leveraging signal
co-processing at multiple access points (APs), aggressive spatial user
multiplexing and extraordinary macro-diversity gain.
In this study, we propose two distributed precoding schemes, referred to as
\textit{local partial zero-forcing} (PZF) and \textit{local protective partial
zero-forcing} (PPZF), that further improve the spectral efficiency by providing
an adaptable trade-off between interference cancelation and boosting of the
desired signal, with no additional front-hauling overhead, and implementable by
APs with very few antennas.
We derive closed-form expressions for the achievable SE under the assumption
of independent Rayleigh fading channel, channel estimation error and pilot
contamination. PZF and PPZF can substantially outperform maximum ratio
transmission and zero-forcing, and their performance is comparable to that
achieved by regularized zero-forcing (RZF), which is a benchmark in the
downlink. Importantly, these closed-form expressions can be employed to devise
optimal (long-term) power control strategies that are also suitable for RZF,
whose closed-form expression for the SE is not available.Comment: This paper was accepted for publication in IEEE Transactions on
Wireless Communications on March 31, 2020. {\copyright} 2020 IEEE. Personal
use of this material is permitted. Permission from IEEE must be obtained for
all other use
On the Performance of Cell-Free Massive MIMO with Short-Term Power Constraints
In this paper we consider a time-division duplex cell-free massive
multiple-input multiple-output (MIMO) system where many distributed access
points (APs) simultaneously serve many users. A normalized conjugate
beamforming scheme, which satisfies short-term average power constraints at the
APs, is proposed and analyzed taking into account the effect of imperfect
channel information. We derive an approximate closed-form expression for the
per-user achievable downlink rate of this scheme. We also provide, analytically
and numerically, a performance comparison between the normalized conjugate
beamforming and the conventional conjugate beamforming scheme in [1] (which
satisfies long-term average power constraints). Normalized conjugate
beamforming scheme reduces the beamforming uncertainty gain, which comes from
the users' lack of the channel state information knowledge, and hence, it
improves the achievable downlink rate compared to the conventional conjugate
beamforming scheme.Comment: 6 pages, 4 figures, 21st IEEE International Workshop on Computer
Aided Modelling and Design of Communication Links and Networks (CAMAD).
Special Session - 5Gwireless: Innovative Architectures, Wireless Technologies
and Tools for High Capacity and Sustainable 5G Ultra-Dense Cellular Network
Levels of Heavy Metals in Adolescents Living in the Industrialised Area of Milazzo-Valle del Mela (Northern Sicily)
In the Milazzo-Valle del Mela area, the presence of industrial plants and the oil refinery make local residents concerned for their health. For this reason, we evaluated the levels of heavy metals in 226 children aged 12–14 years, living in the 7 municipalities of the area. A control age-matched population (n=29) living 45 km far from the industrial site was also enrolled. Arsenic, cadmium, chromium, mercury, nickel, and vanadium were analysed in 24 h urine samples, while lead concentration was evaluated in blood samples. A questionnaire regarding life style and risk perception was also administered. Adolescents from Milazzo-Valle del Mela had cadmium levels significantly higher compared to either controls (P<0.0001) or the reference values of the European Germany Environmental Survey (GerES-IV) and the American National Health and Nutrition Examination Survey (NHANES). Furthermore, children had higher perception of living in a high-risk environment. The present data, for the first time, clearly indicate that adolescents living in Milazzo-Valle del Mela have increased body concentration of cadmium, which may be harmful to human health. These results deserve particular attention by the local and regional government to initiate prevention programmes in this susceptible population