137 research outputs found
On the Achievable Rates of Decentralized Equalization in Massive MU-MIMO Systems
Massive multi-user (MU) multiple-input multiple-output (MIMO) promises
significant gains in spectral efficiency compared to traditional, small-scale
MIMO technology. Linear equalization algorithms, such as zero forcing (ZF) or
minimum mean-square error (MMSE)-based methods, typically rely on centralized
processing at the base station (BS), which results in (i) excessively high
interconnect and chip input/output data rates, and (ii) high computational
complexity. In this paper, we investigate the achievable rates of decentralized
equalization that mitigates both of these issues. We consider two distinct BS
architectures that partition the antenna array into clusters, each associated
with independent radio-frequency chains and signal processing hardware, and the
results of each cluster are fused in a feedforward network. For both
architectures, we consider ZF, MMSE, and a novel, non-linear equalization
algorithm that builds upon approximate message passing (AMP), and we
theoretically analyze the achievable rates of these methods. Our results
demonstrate that decentralized equalization with our AMP-based methods incurs
no or only a negligible loss in terms of achievable rates compared to that of
centralized solutions.Comment: Will be presented at the 2017 IEEE International Symposium on
Information Theor
Beyond Massive MIMO : Trade-offs and Opportunities with Large Multi-Antenna Systems
After the commercial emergence of 5G, the research community is already putting its focus on proposing innovative solutions to enable the upcoming 6G. One important lesson put forth by 5G research was that scaling up the conventional multiple-input-multiple-output (MIMO) technology by increasing the number of antennas could be extremely beneficial for effectively multiplexing data streams in the spatial domain. This idea was embodied in massive MIMO, which constitutes one of the major technical advancements included in 5G. Consequently, 6G research efforts have been largely directed towards studying ways to further scale up wireless systems, as can be seen in some of the proposed 6G enabling technologies like large intelligent surface (LIS), cell-free massive MIMO, or even reconfigurable intelligent surface (RIS). This thesis studies the possibilities offered by some of these technologies, as well as the trade-offs that may naturally arise when scaling up such wireless systems.An important part of this thesis deals with decentralized solutions for base station (BS) technologies including a large number of antennas. Already in the initial massive MIMO prototypes, the increased number of BS antennas led to scalability issues due to the high interconnection bandwidths required to send the received signals---as well as the channel state information (CSI)---to a central processing unit (CPU) in charge of the data processing. These issues can only be exacerbated if we consider novel system proposals like LIS, where the number of BS antennas may be increased by an order of magnitude with respect to massive MIMO, or cell-free massive MIMO, where the BS antennas may be located far from each other. We provide a number of decentralized schemes to process the received data while restricting the information that has to be shared with a CPU. We also provide a framework to study architectures with an arbitrary level of decentralization, showing that there exists a direct trade-off between the interconnection bandwidth to a CPU and the complexity of the decentralized processing required for fixed user rates.Another part of this thesis studies RIS-based solutions to enhance the multiplexing performance of wireless communication systems. RIS constitutes one of the most attractive 6G enabling technologies since it provides a cost- and energy-efficient solution to improve the wireless propagation links by generating favorable reflections. We extend the concept of RIS by considering reconfigurable surfaces (RSs) with different processing capabilities, and we show how these surfaces may be employed for achieving perfect spatial multiplexing at reduced processing complexity in general multi-antenna communication settings. We also show that these surfaces can exploit the available degrees of freedom---e.g., due to excess of BS antennas---to embed their own data into the enhanced channel
Trade-Offs in Decentralized Multi-Antenna Architectures: Sparse Combining Modules for WAX Decomposition
With the increase in the number of antennas at base stations (BSs),
centralized multi-antenna architectures have encountered scalability problems
from excessive interconnection bandwidth to the central processing unit (CPU),
as well as increased processing complexity. Thus, research efforts have been
directed towards finding decentralized receiver architectures where a part of
the processing is performed at the antenna end (or close to it). A recent paper
put forth an information-lossless trade-off between level of decentralization
(inputs to CPU) and decentralized processing complexity (multiplications per
antenna). This trade-off was obtained by studying a newly defined matrix
decomposition--the WAX decomposition--which is directly related to the
information-lossless processing that should to be applied in a general
framework to exploit the trade-off. {The general framework consists of three
stages: a set of decentralized filters, a linear combining module, and a
processing matrix applied at the CPU; these three stages are linear
transformations which can be identified with the three constituent matrices of
the WAX decomposition. The previous work was unable to provide explicit
constructions for linear combining modules which are valid for WAX
decomposition, while it remarked the importance of these modules being sparse
with 1s and 0s so they could be efficiently implemented using hardware
accelerators.} In this work we present a number of constructions, as well as
possible variations of them, for effectively defining linear combining modules
which can be used in the WAX decomposition. Furthermore, we show how these
structures facilitate decentralized calculation of the WAX decomposition for
applying information-lossless processing in architectures with an arbitrary
level of decentralization.Comment: 16 pages, 6 figures, accepted for publication at IEEE Transactions on
Signal Processin
Systems with Massive Number of Antennas: Distributed Approaches
As 5G is entering maturity, the research interest has shifted towards 6G, and specially the new use cases that the future telecommunication infrastructure needs to support. These new use cases encompass much higher requirements, specifically: higher communication data-rates, larger number of users, higher accuracy in localization, possibility to wirelessly charge devices, among others.The radio access network (RAN) has already gone through an evolution on the path towards 5G. One of the main changes was a large increment of the number of antennas in the base-station. Some of them may even reach 100 elements, in what is commonly referred as Massive MIMO. New proposals for 6G RAN point in the direction of continuing this path of increasing the number of antennas, and locate them throughout a certain area of service. Different technologies have been proposed in this direction, such as: cell-free Massive MIMO, distributed MIMO, and large intelligent surface (LIS). In this thesis we focus on LIS, whose conducted theoretical studies promise the fulfillment of the aforementioned requirements.While the theoretical capabilities of LIS have been conveniently analyzed, little has been done in terms of implementing this type of systems. When the number of antennas grow to hundreds or thousands, there are numerous challenges that need to be solved for a successful implementation. The most critical challenges are the interconnection data-rate and the computational complexity.In the present thesis we introduce the implementation challenges, and show that centralized processing architectures are no longer adequate for this type of systems. We also present different distributed processing architectures and show the benefits of this type of schemes. This work aims at giving a system-design guideline that helps the system designer to make the right decisions when designing these type of systems. For that, we provide algorithms, performance analysis and comparisons, including first order evaluation of the interconnection data-rate, processing latency, memory and energy consumption. These numbers are based on models and available data in the literature. Exact values depend on the selected technology, and will be accurately determined after building and testing these type of systems.The thesis concentrates mostly on the topic of communication, with additional exploration of other areas, such as localization. In case of localization, we benefit from the high spatial resolution of a very-large array that provides very rich channel state information (CSI). A CSI-based fingerprinting via neural network technique is selected for this case with promising results. As the communication and localization services are based on the acquisition of CSI, we foresee a common system architecture capable of supporting both cases. Further work in this direction is recommended, with the possibility of including other applications such as sensing.The obtained results indicate that the implementation of these very-large array systems is feasible, but the challenges are numerous. The proposed solutions provide encouraging results that need to be verified with hardware implementations and real measurements
Efficient DSP and Circuit Architectures for Massive MIMO: State-of-the-Art and Future Directions
Massive MIMO is a compelling wireless access concept that relies on the use
of an excess number of base-station antennas, relative to the number of active
terminals. This technology is a main component of 5G New Radio (NR) and
addresses all important requirements of future wireless standards: a great
capacity increase, the support of many simultaneous users, and improvement in
energy efficiency. Massive MIMO requires the simultaneous processing of signals
from many antenna chains, and computational operations on large matrices. The
complexity of the digital processing has been viewed as a fundamental obstacle
to the feasibility of Massive MIMO in the past. Recent advances on
system-algorithm-hardware co-design have led to extremely energy-efficient
implementations. These exploit opportunities in deeply-scaled silicon
technologies and perform partly distributed processing to cope with the
bottlenecks encountered in the interconnection of many signals. For example,
prototype ASIC implementations have demonstrated zero-forcing precoding in real
time at a 55 mW power consumption (20 MHz bandwidth, 128 antennas, multiplexing
of 8 terminals). Coarse and even error-prone digital processing in the antenna
paths permits a reduction of consumption with a factor of 2 to 5. This article
summarizes the fundamental technical contributions to efficient digital signal
processing for Massive MIMO. The opportunities and constraints on operating on
low-complexity RF and analog hardware chains are clarified. It illustrates how
terminals can benefit from improved energy efficiency. The status of technology
and real-life prototypes discussed. Open challenges and directions for future
research are suggested.Comment: submitted to IEEE transactions on signal processin
Benchmarking End-to-end Learning of MIMO Physical-Layer Communication
End-to-end data-driven machine learning (ML) of multiple-input
multiple-output (MIMO) systems has been shown to have the potential of
exceeding the performance of engineered MIMO transceivers, without any a priori
knowledge of communication-theoretic principles. In this work, we aim to
understand to what extent and for which scenarios this claim holds true when
comparing with fair benchmarks. We study closed-loop MIMO, open-loop MIMO, and
multi-user MIMO and show that the gains of ML-based communication in the former
two cases can be to a large extent ascribed to implicitly learned geometric
shaping and bit and power allocation, not to learning new spatial encoders. For
MU-MIMO, we demonstrate the feasibility of a novel method with centralized
learning and decentralized executing, outperforming conventional zero-forcing.
For each scenario, we provide explicit descriptions as well as open-source
implementations of the selected neural-network architectures.Comment: 6 pages, 8 figures, conference pape
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