91 research outputs found
On the Total Energy Efficiency of Cell-Free Massive MIMO
We consider the cell-free massive multiple-input multiple-output (MIMO)
downlink, where a very large number of distributed multiple-antenna access
points (APs) serve many single-antenna users in the same time-frequency
resource. A simple (distributed) conjugate beamforming scheme is applied at
each AP via the use of local channel state information (CSI). This CSI is
acquired through time-division duplex operation and the reception of uplink
training signals transmitted by the users. We derive a closed-form expression
for the spectral efficiency taking into account the effects of channel
estimation errors and power control. This closed-form result enables us to
analyze the effects of backhaul power consumption, the number of APs, and the
number of antennas per AP on the total energy efficiency, as well as, to design
an optimal power allocation algorithm. The optimal power allocation algorithm
aims at maximizing the total energy efficiency, subject to a per-user spectral
efficiency constraint and a per-AP power constraint. Compared with the equal
power control, our proposed power allocation scheme can double the total energy
efficiency. Furthermore, we propose AP selections schemes, in which each user
chooses a subset of APs, to reduce the power consumption caused by the backhaul
links. With our proposed AP selection schemes, the total energy efficiency
increases significantly, especially for large numbers of APs. Moreover, under a
requirement of good quality-of-service for all users, cell-free massive MIMO
outperforms the colocated counterpart in terms of energy efficiency
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
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
Trade-Off Between Beamforming and Macro-Diversity Gains in Distributed mMIMO
Industry and academia have been working towards the evolution from
Centralized massive Multiple-Input Multiple-Output (CmMIMO) to Distributed
mMIMO (DmMIMO) architectures. Instead of splitting a coverage area into many
cells, each served by a single Base Station equipped with several antennas, the
whole coverage area is jointly covered by several Access Points (AP) equipped
with few or single antennas. Nevertheless, when choosing between deploying more
APs with few or single antennas or fewer APs equipped with many antennas, one
observes an inherent trade-off between the beamforming and macro-diversity
gains that has not been investigated in the literature. Given a total number of
antenna elements and total downlink power, under a channel model that takes
into account a probability of Line-of-Sight (LoS) as a function of the distance
between the User Equipments (UEs) and APs, our numerical results show that
there exists a ``sweet spot" on the optimal number of APs and of antenna
elements per AP which is a function of the physical dimensions of the coverage
area.Comment: 6 pages, 3 figures. Manuscript submitted to the IEEE Wireless
Communications and Networking Conference (WCNC) 2024, Dubai, United Arab
Emirate
Joint Power Allocation and Access Point Selection for Cell-free Massive MIMO
Cell-free massive multiple-input multiple-output (CF-MIMO) is a promising technological enabler for fifth generation (5G) networks in which a large number of access points (APs) jointly serve the users. Each AP applies conjugate beamforming to precode data, which is based only on the AP's local channel state information. However, by having the nature of a (very) large number of APs, the operation of CF-MIMO can be energy-inefficient. In this paper, we investigate the energy efficiency performance of CF-MIMO by considering a practical energy consumption model which includes both the signal transmit energy as well as the static energy consumed by hardware components. In particular, a joint power allocation and AP selection design is proposed to minimize the total energy consumption subject to given quality of service (QoS) constraints. In order to deal with the combinatorial complexity of the formulated problem, we employ norm -based block-sparsity and successive convex optimization to leverage the AP selection process. Numerical results show significant energy savings obtained by the proposed design, compared to all-active APs scheme and the large-scale based AP selection
On the Sum Secrecy Rate Maximisation for Wireless Vehicular Networks
Wireless communications form the backbone of future vehicular networks,
playing a critical role in applications ranging from traffic control to
vehicular road safety. However, the dynamic structure of these networks creates
security vulnerabilities, making security considerations an integral part of
network design. We address these security concerns from a physical layer
security aspect by investigating achievable secrecy rates in wireless vehicular
networks. Specifically, we aim to maximize the sum secrecy rate from all
vehicular pairs subject to bandwidth and power resource constraints. For the
considered problem, we first propose a solution based on the successive convex
approximation (SCA) method, which has not been applied in this context before.
To further reduce the complexity of the SCA-based method, we also propose a
low-complexity solution based on a fast iterative shrinkage-thresholding
algorithm (FISTA). Our simulation results for SCA and FISTA show a trade-off
between convergence and runtime. While the SCA method achieves better
convergence, the FISTA-based approach is at least 300 times faster than the SCA
method
- …