82 research outputs found
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
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
Processing Distribution and Architecture Tradeoff for Large Intelligent Surface Implementation
The Large Intelligent Surface (LIS) concept has emerged recently as a new
paradigm for wireless communication, remote sensing and positioning. It
consists of a continuous radiating surface placed relatively close to the
users, which is able to communicate with users by independent transmission and
reception (replacing base stations). Despite of its potential, there are a lot
of challenges from an implementation point of view, with the interconnection
data-rate and computational complexity being the most relevant. Distributed
processing techniques and hierarchical architectures are expected to play a
vital role addressing this while ensuring scalability. In this paper we perform
algorithm-architecture codesign and analyze the hardware requirements and
architecture trade-offs for a discrete LIS to perform uplink detection. By
doing this, we expect to give concrete case studies and guidelines for
efficient implementation of LIS systems.Comment: Presented at IEEE ICC 202
Recent Advances in Acquiring Channel State Information in Cellular MIMO Systems
In cellular multi-user multiple input multiple output (MU-MIMO) systems the quality of the available channel state information (CSI) has a large impact on the system performance. Specifically, reliable CSI at the transmitter is required to determine the appropriate modulation and coding scheme, transmit power and the precoder vector, while CSI at the receiver is needed to decode the received data symbols. Therefore, cellular MUMIMO systems employ predefined pilot sequences and configure associated time, frequency, code and power resources to facilitate the acquisition of high quality CSI for data transmission and reception. Although the trade-off between the resources used user data transmission has been known for long, the near-optimal configuration of the vailable system resources for pilot and data transmission is a topic of current research efforts. Indeed, since the fifth generation of cellular systems utilizes heterogeneous networks in which base stations are equipped with a large number of transmit and receive antennas, the appropriate configuration of pilot-data resources becomes a critical design aspect. In this article, we review recent advances in system design approaches that are designed for the acquisition of CSI and discuss some of the recent results that help to dimension the pilot and data resources specifically in cellular MU-MIMO systems
Trade-offs In Quasi-Decentralized Massive MIMO
Typical massive multiple-input multiple-output (MIMO) architectures consider
a centralized approach, in which all baseband data received by each antenna has
to be sent to a central processing unit (CPU) to be processed. Due to the
enormous amount of antennas expected in massive MIMO base stations (BSs), the
number of connections to the CPU required in centralized massive MIMO is not
scalable. In recent literature decentralized approaches have been proposed to
reduce the number of connections between the antennas and the CPU. However, the
reduction in the connections to the CPU requires more outputs per antenna to be
generated. We study the trade-off between number of connections to the CPU and
number of outputs per antenna. We propose a generalized architecture that
allows exploitation of this trade-off, and we define a novel matrix
decomposition that allows lossless linear equalization within our proposed
architecture.Comment: 6 pages, 4 figures, accepted at IEEE ICC 2020 workshop on scalable
massive MIMO technologies for beyond 5
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
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
Hardware topologies for decentralized large-scale MIMO detection using Newton method
Centralized Massive Multiple Input Multiple Output (MIMO) uplink detection techniques for baseband processing possess severe bottleneck in terms of interconnect bandwidth and computational complexity. This problem has been addressed in the current work by adapting the centralized Newton method for decentralized MIMO uplink detection leveraging several Base Station antenna clusters. The proposed decentralized Newton (DN) method achieves error-rate performance close to centralized Zero Forcing detector as compared to other decentralized techniques. Two hardware topologies, namely the ring and the star topologies, are proposed to assess and discuss the trade-off among interconnect bandwidth and throughput, in comparison with contemporary decentralized MIMO uplink detection techniques. As such the following findings are elaborated. On BS antenna cluster scaling for different MIMO system configurations, the ring topology provides high throughput at constant interconnect bandwidth, while the star topology provides lower latency with a deterministic variation in the hardware resource consumption. Due to strategic optimizations on the hardware implementation, additional user equipment can be allotted at a fractional increase in Field Programmable Gate Array resource consumption, improved energy efficiency, and increased transaction of bits per Joule. The ring topology can process additional subcarrier at a fractional increase in latency and improved system throughput
Trade-offs in Decentralized Multi-Antenna Architectures: The WAX Decomposition
Current research on multi-antenna architectures is trending towards
increasing the amount of antennas in the base stations (BSs) so as to increase
the spectral efficiency. As a result, the interconnection bandwidth and
computational complexity required to process the data using centralized
architectures is becoming prohibitively high. Decentralized architectures can
reduce these requirements by pre-processing the data before it arrives at a
central processing unit (CPU). However, performing decentralized processing
introduces also cost in complexity/interconnection bandwidth at the antenna end
which is in general being ignored. This paper aims at studying the interplay
between level of decentralization and the associated complexity/interconnection
bandwidth requirement at the antenna end. To do so, we propose a general
framework for centralized/decentralized architectures that can explore said
interplay by adjusting some system parameters, namely the number of connections
to the CPU (level of decentralization), and the number of
multiplications/outputs per antenna (complexity/interconnection bandwidth). We
define a novel matrix decomposition, the WAX decomposition, that allows
information-lossless processing within our proposed framework, and we use it to
obtain the operational limits of the interplay under study. We also look into
some of the limitations of the WAX decomposition.Comment: 14 pages, 9 figures, submitted to IEEE Transactions on Signal
Processing. arXiv admin note: text overlap with arXiv:2003.0196
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