9 research outputs found
Joint Analog Beam Selection and Digital Beamforming in Millimeter Wave Cell-Free Massive MIMO Systems
Cell-free massive MIMO systems consist of many distributed access points with
simple components that jointly serve the users. In millimeter wave bands, only
a limited set of predetermined beams can be supported. In a network that
consolidates these technologies, downlink analog beam selection stands as a
challenging task for the network sum-rate maximization. Low-cost digital
filters can improve the network sum-rate further. In this work, we propose
low-cost joint designs of analog beam selection and digital filters. The
proposed joint designs achieve significantly higher sum-rates than the disjoint
design benchmark. Supervised machine learning (ML) algorithms can efficiently
approximate the input-output mapping functions of the beam selection decisions
of the joint designs with low computational complexities. Since the training of
ML algorithms is performed off-line, we propose a well-constructed joint design
that combines multiple initializations, iterations, and selection features, as
well as beam conflict control, i.e., the same beam cannot be used for multiple
users. The numerical results indicate that ML algorithms can retain 99-100% of
the original sum-rate results achieved by the proposed well-constructed
designs.Comment: 14 pages, 11 figures. First submission date: August 19th, 2020. To be
published at IEEE Open Journal of the Communications Societ
Transmit Optimization with Improper Gaussian Signaling for Interference Channels
This paper studies the achievable rates of Gaussian interference channels
with additive white Gaussian noise (AWGN), when improper or circularly
asymmetric complex Gaussian signaling is applied. For the Gaussian
multiple-input multiple-output interference channel (MIMO-IC) with the
interference treated as Gaussian noise, we show that the user's achievable rate
can be expressed as a summation of the rate achievable by the conventional
proper or circularly symmetric complex Gaussian signaling in terms of the
users' transmit covariance matrices, and an additional term, which is a
function of both the users' transmit covariance and pseudo-covariance matrices.
The additional degrees of freedom in the pseudo-covariance matrix, which is
conventionally set to be zero for the case of proper Gaussian signaling,
provide an opportunity to further improve the achievable rates of Gaussian
MIMO-ICs by employing improper Gaussian signaling. To this end, this paper
proposes widely linear precoding, which efficiently maps proper
information-bearing signals to improper transmitted signals at each transmitter
for any given pair of transmit covariance and pseudo-covariance matrices. In
particular, for the case of two-user Gaussian single-input single-output
interference channel (SISO-IC), we propose a joint covariance and
pseudo-covariance optimization algorithm with improper Gaussian signaling to
achieve the Pareto-optimal rates. By utilizing the separable structure of the
achievable rate expression, an alternative algorithm with separate covariance
and pseudo-covariance optimization is also proposed, which guarantees the rate
improvement over conventional proper Gaussian signaling.Comment: Accepted by IEEE Transactions on Signal Processin
Sub-Stream Fairness and Numerical Correctness in MIMO Interference Channels
Signal-to-interference plus noise ratio (SINR) and rate fairness in a system
are substantial quality-of-service (QoS) metrics. The acclaimed SINR
maximization (max-SINR) algorithm does not achieve fairness between user's
streams, i.e., sub-stream fairness is not achieved. To this end, we propose a
distributed power control algorithm to render sub-stream fairness in the
system. Sub-stream fairness is a less restrictive design metric than stream
fairness (i.e., fairness between all streams) thus sum-rate degradation is
milder. Algorithmic parameters can significantly differentiate the results of
numerical algorithms. A complete picture for comparison of algorithms can only
be depicted by varying these parameters. For example, a predetermined iteration
number or a negligible increment in the sum-rate can be the stopping criteria
of an algorithm. While the distributed interference alignment (DIA) can
reasonably achieve sub-stream fairness for the later, the imbalance between
sub-streams increases as the preset iteration number decreases. Thus comparison
of max-SINR and DIA with a low preset iteration number can only depict a part
of the picture. We analyze such important parameters and their effects on SINR
and rate metrics to exhibit numerical correctness in executing the benchmarks.
Finally, we propose group filtering schemes that jointly design the streams of
a user in contrast to max-SINR scheme that designs each stream of a user
separately.Comment: To be presented at IEEE ISWTA'1
Improving Achievable Rate for the Two-User SISO Interference Channel with Improper Gaussian Signaling
This paper studies the achievable rate region of the two-user
single-input-single-output (SISO) Gaussian interference channel, when the
improper Gaussian signaling is applied. Under the assumption that the
interference is treated as additive Gaussian noise, we show that the user's
achievable rate can be expressed as a summation of the rate achievable by the
conventional proper Gaussian signaling, which depends on the users' input
covariances only, and an additional term, which is a function of both the
users' covariances and pseudo-covariances. The additional degree of freedom
given by the pseudo-covariance, which is conventionally set to be zero for the
case of proper Gaussian signaling, provides an opportunity to improve the
achievable rate by employing the improper Gaussian signaling. Since finding the
optimal solution for the joint covariance and pseudo-covariance optimization is
difficult, we propose a sub-optimal but efficient algorithm by separately
optimizing these two sets of parameters. Numerical results show that the
proposed algorithm provides a close-to-optimal performance as compared to the
exhaustive search method, and significantly outperforms the optimal proper
Gaussian signaling and other existing improper Gaussian signaling schemes.Comment: Version 2, Invited paper, submitted to Asilomar 201
Interference alignment testbeds
Interference alignment has triggered high impact research in wireless communications since it was proposed nearly 10 years ago. However, the vast majority of research is centered on the theory of interference alignment and is hardly feasible in view of the existing state-of-the-art wireless technologies. Although several research groups have assessed the feasibility of interference alignment via testbed measurements in realistic environments, the experimental evaluation of interference alignment is still in its infancy since most of the experiments were limited to simpler scenarios and configurations. This article summarizes the practical limitations of experimentally evaluating interference alignment, provides an overview of the available interference alignment testbed implementations, including the costs, and highlights the imperatives for succeeding interference alignment testbed implementations. Finally, the article explores future research directions on the applications of interference alignment in the next generation wireless systems.Jacobo Fanjul's research has been supported by the Ministerio de Economía y Competitividad (MINECO) of Spain, under grants TEC2013-47141-C4-R (RACHEL project) and FPI grant BES-2014-069786. José A. García-Naya's research has been funded by the Xunta de Galicia (ED431C 2016–045, ED341D R2016/012, E0431 G/01), the Agencia Estatal de Investigación of Spain (TEC2013-47141-C4-1-R, TEC2015-69648-REOC, TEC2016-75067-C4-1-R), and ERDF funds of the EU (AEI/FEDER, UE). Hamed Farhadi's research has been funded by the Swedish Research Council (VR) under grant 2015–00500