2,976 research outputs found
Rate Splitting for MIMO Wireless Networks: A Promising PHY-Layer Strategy for LTE Evolution
MIMO processing plays a central part towards the recent increase in spectral
and energy efficiencies of wireless networks. MIMO has grown beyond the
original point-to-point channel and nowadays refers to a diverse range of
centralized and distributed deployments. The fundamental bottleneck towards
enormous spectral and energy efficiency benefits in multiuser MIMO networks
lies in a huge demand for accurate channel state information at the transmitter
(CSIT). This has become increasingly difficult to satisfy due to the increasing
number of antennas and access points in next generation wireless networks
relying on dense heterogeneous networks and transmitters equipped with a large
number of antennas. CSIT inaccuracy results in a multi-user interference
problem that is the primary bottleneck of MIMO wireless networks. Looking
backward, the problem has been to strive to apply techniques designed for
perfect CSIT to scenarios with imperfect CSIT. In this paper, we depart from
this conventional approach and introduce the readers to a promising strategy
based on rate-splitting. Rate-splitting relies on the transmission of common
and private messages and is shown to provide significant benefits in terms of
spectral and energy efficiencies, reliability and CSI feedback overhead
reduction over conventional strategies used in LTE-A and exclusively relying on
private message transmissions. Open problems, impact on standard specifications
and operational challenges are also discussed.Comment: accepted to IEEE Communication Magazine, special issue on LTE
Evolutio
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
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
Massive MIMO for Internet of Things (IoT) Connectivity
Massive MIMO is considered to be one of the key technologies in the emerging
5G systems, but also a concept applicable to other wireless systems. Exploiting
the large number of degrees of freedom (DoFs) of massive MIMO essential for
achieving high spectral efficiency, high data rates and extreme spatial
multiplexing of densely distributed users. On the one hand, the benefits of
applying massive MIMO for broadband communication are well known and there has
been a large body of research on designing communication schemes to support
high rates. On the other hand, using massive MIMO for Internet-of-Things (IoT)
is still a developing topic, as IoT connectivity has requirements and
constraints that are significantly different from the broadband connections. In
this paper we investigate the applicability of massive MIMO to IoT
connectivity. Specifically, we treat the two generic types of IoT connections
envisioned in 5G: massive machine-type communication (mMTC) and ultra-reliable
low-latency communication (URLLC). This paper fills this important gap by
identifying the opportunities and challenges in exploiting massive MIMO for IoT
connectivity. We provide insights into the trade-offs that emerge when massive
MIMO is applied to mMTC or URLLC and present a number of suitable communication
schemes. The discussion continues to the questions of network slicing of the
wireless resources and the use of massive MIMO to simultaneously support IoT
connections with very heterogeneous requirements. The main conclusion is that
massive MIMO can bring benefits to the scenarios with IoT connectivity, but it
requires tight integration of the physical-layer techniques with the protocol
design.Comment: Submitted for publicatio
On the Performance Gain of NOMA over OMA in Uplink Communication Systems
In this paper, we investigate and reveal the ergodic sum-rate gain (ESG) of
non-orthogonal multiple access (NOMA) over orthogonal multiple access (OMA) in
uplink cellular communication systems. A base station equipped with a
single-antenna, with multiple antennas, and with massive antenna arrays is
considered both in single-cell and multi-cell deployments. In particular, in
single-antenna systems, we identify two types of gains brought about by NOMA:
1) a large-scale near-far gain arising from the distance discrepancy between
the base station and users; 2) a small-scale fading gain originating from the
multipath channel fading. Furthermore, we reveal that the large-scale near-far
gain increases with the normalized cell size, while the small-scale fading gain
is a constant, given by = 0.57721 nat/s/Hz, in Rayleigh fading
channels. When extending single-antenna NOMA to -antenna NOMA, we prove that
both the large-scale near-far gain and small-scale fading gain achieved by
single-antenna NOMA can be increased by a factor of for a large number of
users. Moreover, given a massive antenna array at the base station and
considering a fixed ratio between the number of antennas, , and the number
of users, , the ESG of NOMA over OMA increases linearly with both and
. We then further extend the analysis to a multi-cell scenario. Compared to
the single-cell case, the ESG in multi-cell systems degrades as NOMA faces more
severe inter-cell interference due to the non-orthogonal transmissions.
Besides, we unveil that a large cell size is always beneficial to the ergodic
sum-rate performance of NOMA in both single-cell and multi-cell systems.
Numerical results verify the accuracy of the analytical results derived and
confirm the insights revealed about the ESG of NOMA over OMA in different
scenarios.Comment: 51 pages, 7 figures, invited paper, submitted to IEEE Transactions on
Communication
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