1,951 research outputs found
Cooperative Multi-Cell Networks: Impact of Limited-Capacity Backhaul and Inter-Users Links
Cooperative technology is expected to have a great impact on the performance
of cellular or, more generally, infrastructure networks. Both multicell
processing (cooperation among base stations) and relaying (cooperation at the
user level) are currently being investigated. In this presentation, recent
results regarding the performance of multicell processing and user cooperation
under the assumption of limited-capacity interbase station and inter-user
links, respectively, are reviewed. The survey focuses on related results
derived for non-fading uplink and downlink channels of simple cellular system
models. The analytical treatment, facilitated by these simple setups, enhances
the insight into the limitations imposed by limited-capacity constraints on the
gains achievable by cooperative techniques
Spectrum Sharing in mmWave Cellular Networks via Cell Association, Coordination, and Beamforming
This paper investigates the extent to which spectrum sharing in mmWave
networks with multiple cellular operators is a viable alternative to
traditional dedicated spectrum allocation. Specifically, we develop a general
mathematical framework by which to characterize the performance gain that can
be obtained when spectrum sharing is used, as a function of the underlying
beamforming, operator coordination, bandwidth, and infrastructure sharing
scenarios. The framework is based on joint beamforming and cell association
optimization, with the objective of maximizing the long-term throughput of the
users. Our asymptotic and non-asymptotic performance analyses reveal five key
points: (1) spectrum sharing with light on-demand intra- and inter-operator
coordination is feasible, especially at higher mmWave frequencies (for example,
73 GHz), (2) directional communications at the user equipment substantially
alleviate the potential disadvantages of spectrum sharing (such as higher
multiuser interference), (3) large numbers of antenna elements can reduce the
need for coordination and simplify the implementation of spectrum sharing, (4)
while inter-operator coordination can be neglected in the large-antenna regime,
intra-operator coordination can still bring gains by balancing the network
load, and (5) critical control signals among base stations, operators, and user
equipment should be protected from the adverse effects of spectrum sharing, for
example by means of exclusive resource allocation. The results of this paper,
and their extensions obtained by relaxing some ideal assumptions, can provide
important insights for future standardization and spectrum policy.Comment: 15 pages. To appear in IEEE JSAC Special Issue on Spectrum Sharing
and Aggregation for Future Wireless Network
The Practical Challenges of Interference Alignment
Interference alignment (IA) is a revolutionary wireless transmission strategy
that reduces the impact of interference. The idea of interference alignment is
to coordinate multiple transmitters so that their mutual interference aligns at
the receivers, facilitating simple interference cancellation techniques. Since
IA's inception, researchers have investigated its performance and proposed
improvements, verifying IA's ability to achieve the maximum degrees of freedom
(an approximation of sum capacity) in a variety of settings, developing
algorithms for determining alignment solutions, and generalizing transmission
strategies that relax the need for perfect alignment but yield better
performance. This article provides an overview of the concept of interference
alignment as well as an assessment of practical issues including performance in
realistic propagation environments, the role of channel state information at
the transmitter, and the practicality of interference alignment in large
networks.Comment: submitted to IEEE Wireless Communications Magazin
Receive Combining vs. Multi-Stream Multiplexing in Downlink Systems with Multi-Antenna Users
In downlink multi-antenna systems with many users, the multiplexing gain is
strictly limited by the number of transmit antennas and the use of these
antennas. Assuming that the total number of receive antennas at the
multi-antenna users is much larger than , the maximal multiplexing gain can
be achieved with many different transmission/reception strategies. For example,
the excess number of receive antennas can be utilized to schedule users with
effective channels that are near-orthogonal, for multi-stream multiplexing to
users with well-conditioned channels, and/or to enable interference-aware
receive combining. In this paper, we try to answer the question if the data
streams should be divided among few users (many streams per user) or many users
(few streams per user, enabling receive combining). Analytic results are
derived to show how user selection, spatial correlation, heterogeneous user
conditions, and imperfect channel acquisition (quantization or estimation
errors) affect the performance when sending the maximal number of streams or
one stream per scheduled user---the two extremes in data stream allocation.
While contradicting observations on this topic have been reported in prior
works, we show that selecting many users and allocating one stream per user
(i.e., exploiting receive combining) is the best candidate under realistic
conditions. This is explained by the provably stronger resilience towards
spatial correlation and the larger benefit from multi-user diversity. This
fundamental result has positive implications for the design of downlink systems
as it reduces the hardware requirements at the user devices and simplifies the
throughput optimization.Comment: Published in IEEE Transactions on Signal Processing, 16 pages, 11
figures. The results can be reproduced using the following Matlab code:
https://github.com/emilbjornson/one-or-multiple-stream
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