787 research outputs found
A Generalized Framework on Beamformer Design and CSI Acquisition for Single-Carrier Massive MIMO Systems in Millimeter Wave Channels
In this paper, we establish a general framework on the reduced dimensional
channel state information (CSI) estimation and pre-beamformer design for
frequency-selective massive multiple-input multiple-output MIMO systems
employing single-carrier (SC) modulation in time division duplex (TDD) mode by
exploiting the joint angle-delay domain channel sparsity in millimeter (mm)
wave frequencies. First, based on a generic subspace projection taking the
joint angle-delay power profile and user-grouping into account, the reduced
rank minimum mean square error (RR-MMSE) instantaneous CSI estimator is derived
for spatially correlated wideband MIMO channels. Second, the statistical
pre-beamformer design is considered for frequency-selective SC massive MIMO
channels. We examine the dimension reduction problem and subspace (beamspace)
construction on which the RR-MMSE estimation can be realized as accurately as
possible. Finally, a spatio-temporal domain correlator type reduced rank
channel estimator, as an approximation of the RR-MMSE estimate, is obtained by
carrying out least square (LS) estimation in a proper reduced dimensional
beamspace. It is observed that the proposed techniques show remarkable
robustness to the pilot interference (or contamination) with a significant
reduction in pilot overhead
Baseband Processing for 5G and Beyond: Algorithms, VLSI Architectures, and Co-design
In recent years the number of connected devices and the demand for high data-rates have been significantly increased. This enormous growth is more pronounced by the introduction of the Internet of things (IoT) in which several devices are interconnected to exchange data for various applications like smart homes and smart cities. Moreover, new applications such as eHealth, autonomous vehicles, and connected ambulances set new demands on the reliability, latency, and data-rate of wireless communication systems, pushing forward technology developments. Massive multiple-input multiple-output (MIMO) is a technology, which is employed in the 5G standard, offering the benefits to fulfill these requirements. In massive MIMO systems, base station (BS) is equipped with a very large number of antennas, serving several users equipments (UEs) simultaneously in the same time and frequency resource. The high spatial multiplexing in massive MIMO systems, improves the data rate, energy and spectral efficiencies as well as the link reliability of wireless communication systems. The link reliability can be further improved by employing channel coding technique. Spatially coupled serially concatenated codes (SC-SCCs) are promising channel coding schemes, which can meet the high-reliability demands of wireless communication systems beyond 5G (B5G). Given the close-to-capacity error correction performance and the potential to implement a high-throughput decoder, this class of code can be a good candidate for wireless systems B5G. In order to achieve the above-mentioned advantages, sophisticated algorithms are required, which impose challenges on the baseband signal processing. In case of massive MIMO systems, the processing is much more computationally intensive and the size of required memory to store channel data is increased significantly compared to conventional MIMO systems, which are due to the large size of the channel state information (CSI) matrix. In addition to the high computational complexity, meeting latency requirements is also crucial. Similarly, the decoding-performance gain of SC-SCCs also do come at the expense of increased implementation complexity. Moreover, selecting the proper choice of design parameters, decoding algorithm, and architecture will be challenging, since spatial coupling provides new degrees of freedom in code design, and therefore the design space becomes huge. The focus of this thesis is to perform co-optimization in different design levels to address the aforementioned challenges/requirements. To this end, we employ system-level characteristics to develop efficient algorithms and architectures for the following functional blocks of digital baseband processing. First, we present a fast Fourier transform (FFT), an inverse FFT (IFFT), and corresponding reordering scheme, which can significantly reduce the latency of orthogonal frequency-division multiplexing (OFDM) demodulation and modulation as well as the size of reordering memory. The corresponding VLSI architectures along with the application specific integrated circuit (ASIC) implementation results in a 28 nm CMOS technology are introduced. In case of a 2048-point FFT/IFFT, the proposed design leads to 42% reduction in the latency and size of reordering memory. Second, we propose a low-complexity massive MIMO detection scheme. The key idea is to exploit channel sparsity to reduce the size of CSI matrix and eventually perform linear detection followed by a non-linear post-processing in angular domain using the compressed CSI matrix. The VLSI architecture for a massive MIMO with 128 BS antennas and 16 UEs along with the synthesis results in a 28 nm technology are presented. As a result, the proposed scheme reduces the complexity and required memory by 35%–73% compared to traditional detectors while it has better detection performance. Finally, we perform a comprehensive design space exploration for the SC-SCCs to investigate the effect of different design parameters on decoding performance, latency, complexity, and hardware cost. Then, we develop different decoding algorithms for the SC-SCCs and discuss the associated decoding performance and complexity. Also, several high-level VLSI architectures along with the corresponding synthesis results in a 12 nm process are presented, and various design tradeoffs are provided for these decoding schemes
Efficient Detection in Uniform Linear and Planar Arrays MIMO Systems under Spatial Correlated Channels
In this paper, the efficiency of various MIMO detectors was analyzed from the
perspective of highly correlated channels, where MIMO systems have a lack of
performance, besides in some cases an increasing complexity. Considering this
hard, but {a useful} scenario, various MIMO detection schemes {were accurately
evaluated concerning} complexity and bit error rate (BER) performance.
Specifically, successive interference cancellation (SIC), lattice reduction
(LR) and the combination of them were associated with conventional linear MIMO
detection techniques. {To demonstrate effectiveness}, a wide range of the
number of antennas and modulation formats have been considered aiming to verify
the potential of such MIMO detection techniques according to their
performance-complexity trade-off. We have also studied the correlation effect
when both transmit and receiver sides are equipped with uniform linear array
(ULA) and uniform planar array (UPA) antenna configurations. The performance of
different detectors is carefully compared when both antenna array
configurations are deployed {considering} a different number of antennas and
modulation order, especially {under} near-massive MIMO condition. We have also
discussed the relationship between the array factor (AF) and the BER
performance of both {antenna array} structures
Machine Learning for Heterogeneous Ultra-Dense Networks with Graphical Representations
Heterogeneous ultra-dense network (H-UDN) is envisioned as a promising
solution to sustain the explosive mobile traffic demand through network
densification. By placing access points, processors, and storage units as close
as possible to mobile users, H-UDNs bring forth a number of advantages,
including high spectral efficiency, high energy efficiency, and low latency.
Nonetheless, the high density and diversity of network entities in H-UDNs
introduce formidable design challenges in collaborative signal processing and
resource management. This article illustrates the great potential of machine
learning techniques in solving these challenges. In particular, we show how to
utilize graphical representations of H-UDNs to design efficient machine
learning algorithms
Millimeter Wave MIMO with Lens Antenna Array: A New Path Division Multiplexing Paradigm
Millimeter wave (mmWave) communication is a promising technology for 5G
cellular systems. To compensate for the severe path loss in mmWave systems,
large antenna arrays are generally used to achieve significant beamforming
gains. However, due to the high hardware and power consumption cost associated
with radio frequency (RF) chains, it is desirable to achieve the large-antenna
gains, but with only limited number of RF chains for mmWave communications. To
this end, we study in this paper a new lens antenna array enabled mmWave MIMO
communication system. We first show that the array response of the proposed
lens antenna array at the receiver/transmitter follows a "sinc" function, where
the antenna with the peak response is determined by the angle of arrival
(AoA)/departure (AoD) of the received/transmitted signal. By exploiting this
unique property of lens antenna arrays along with the multi-path sparsity of
mmWave channels, we propose a novel low-cost and capacity-achieving MIMO
transmission scheme, termed \emph{orthogonal path division multiplexing
(OPDM)}. For channels with insufficiently separated AoAs and/or AoDs, we also
propose a simple \emph{path grouping} technique with group-based small-scale
MIMO processing to mitigate the inter-path interference. Numerical results are
provided to compare the performance of the proposed lens antenna arrays for
mmWave MIMO system against that of conventional arrays, under different
practical setups. It is shown that the proposed system achieves significant
throughput gain as well as complexity and hardware cost reduction, both making
it an appealing new paradigm for mmWave MIMO communications.Comment: submitted for possible journal publicatio
Massive Access in Cell-Free Massive MIMO-Based Internet of Things: Cloud Computing and Edge Computing Paradigms
This paper studies massive access in cell-free massive multi-input
multi-output (MIMO) based Internet of Things and solves the challenging active
user detection (AUD) and channel estimation (CE) problems. For the uplink
transmission, we propose an advanced frame structure design to reduce the
access latency. Moreover, by considering the cooperation of all access points
(APs), we investigate two processing paradigms at the receiver for massive
access: cloud computing and edge computing. For cloud computing, all APs are
connected to a centralized processing unit (CPU), and the signals received at
all APs are centrally processed at the CPU. While for edge computing, the
central processing is offloaded to part of APs equipped with distributed
processing units, so that the AUD and CE can be performed in a distributed
processing strategy. Furthermore, by leveraging the structured sparsity of the
channel matrix, we develop a structured sparsity-based generalized approximated
message passing (SS-GAMP) algorithm for reliable joint AUD and CE, where the
quantization accuracy of the processed signals is taken into account. Based on
the SS-GAMP algorithm, a successive interference cancellation-based AUD and CE
scheme is further developed under two paradigms for reduced access latency.
Simulation results validate the superiority of the proposed approach over the
state-of-the-art baseline schemes. Besides, the results reveal that the edge
computing can achieve the similar massive access performance as the cloud
computing, and the edge computing is capable of alleviating the burden on CPU,
having a faster access response, and supporting more flexible AP cooperation.Comment: 17 pages, 16 figures. The current version has been accepted by IEEE
Journal on Selected Areas in Communications (JSAC) Special Issue on Massive
Access for 5G and Beyon
Joint Spatial Division and Multiplexing for mm-Wave Channels
Massive MIMO systems are well-suited for mm-Wave communications, as large
arrays can be built with reasonable form factors, and the high array gains
enable reasonable coverage even for outdoor communications. One of the main
obstacles for using such systems in frequency-division duplex mode, namely the
high overhead for the feedback of channel state information (CSI) to the
transmitter, can be mitigated by the recently proposed JSDM (Joint Spatial
Division and Multiplexing) algorithm. In this paper we analyze the performance
of this algorithm in some realistic propagation channels that take into account
the partial overlap of the angular spectra from different users, as well as the
sparsity of mm-Wave channels. We formulate the problem of user grouping for two
different objectives, namely maximizing spatial multiplexing, and maximizing
total received power, in a graph-theoretic framework. As the resulting problems
are numerically difficult, we proposed (sub optimum) greedy algorithms as
efficient solution methods. Numerical examples show that the different
algorithms may be superior in different settings.We furthermore develop a new,
"degenerate" version of JSDM that only requires average CSI at the transmitter,
and thus greatly reduces the computational burden. Evaluations in propagation
channels obtained from ray tracing results, as well as in measured outdoor
channels show that this low-complexity version performs surprisingly well in
mm-Wave channels.Comment: Accepted for publication in "JSAC Special Issue in 5G Communication
Systems
High-Dimensional CSI Acquisition in Massive MIMO: Sparsity-Inspired Approaches
Massive MIMO has been regarded as one of the key technologies for 5G wireless
networks, as it can significantly improve both the spectral efficiency and
energy efficiency. The availability of high-dimensional channel side
information (CSI) is critical for its promised performance gains, but the
overhead of acquiring CSI may potentially deplete the available radio
resources. Fortunately, it has recently been discovered that harnessing various
sparsity structures in massive MIMO channels can lead to significant overhead
reduction, and thus improve the system performance. This paper presents and
discusses the use of sparsity-inspired CSI acquisition techniques for massive
MIMO, as well as the underlying mathematical theory. Sparsity-inspired
approaches for both frequency-division duplexing and time-division duplexing
massive MIMO systems will be examined and compared from an overall system
perspective, including the design trade-offs between the two duplexing modes,
computational complexity of acquisition algorithms, and applicability of
sparsity structures. Meanwhile, some future prospects for research on
high-dimensional CSI acquisition to meet practical demands will be identified.Comment: 15 pages, 3 figures, 1 table, submitted to IEEE Systems Journal
Special Issue on 5G Wireless Systems with Massive MIM
Near-Optimal Hybrid Processing for Massive MIMO Systems via Matrix Decomposition
For the practical implementation of massive multiple-input multiple-output
(MIMO) systems, the hybrid processing (precoding/combining) structure is
promising to reduce the high cost rendered by large number of RF chains of the
traditional processing structure. The hybrid processing is performed through
low-dimensional digital baseband processing combined with analog RF processing
enabled by phase shifters. We propose to design hybrid RF and baseband
precoders/combiners for multi-stream transmission in point-to-point massive
MIMO systems, by directly decomposing the pre-designed unconstrained digital
precoder/combiner of a large dimension. The constant amplitude constraint of
analog RF processing results in the matrix decomposition problem non-convex.
Based on an alternate optimization technique, the non-convex matrix
decomposition problem can be decoupled into a series of convex sub-problems and
effectively solved by restricting the phase increment of each entry in the RF
precoder/combiner within a small vicinity of its preceding iterate. A singular
value decomposition based technique is proposed to secure an initial point
sufficiently close to the global solution of the original non-convex problem.
Through simulation, the convergence of the alternate optimization for such a
matrix decomposition based hybrid processing (MD-HP) scheme is examined, and
the performance of the MD-HP scheme is demonstrated to be near-optimal
Compressive Initial Access and Beamforming Training for Millimeter-Wave Cellular Systems
Initial access (IA) is a fundamental physical layer procedure in cellular
systems where user equipment (UE) detects nearby base station (BS) as well as
acquire synchronization. Due to the necessity of using antenna array in
millimeter-wave (mmW) IA, the channel spatial information can also be inferred.
The state-of-the-art directional IA (DIA) uses sector sounding beams with
limited angular resolution, and thus requires additional dedicated radio
resources, access latency and overhead for refined beam training. To remedy the
problem of access latency and overhead in DIA, this work proposes to use a
quasi-omni pseudorandom sounding beam for IA, and develops a novel algorithm
for joint initial access and fine resolution initial beam training without
requiring extra radio resources. We provide the analysis of the proposed
algorithm miss detection rate under synchronization error, and further derive
Cram\'er-Rao lower bound of angular estimation under frequency offset. Using
QuaDRiGa simulator with mmMAGIC model at 28 GHz, the numerical results show
that the proposed approach is advantageous to DIA with hierarchical beam
training. The proposed algorithm offers up to two order of magnitude access
latency saving compared to DIA, when the same discovery, post training SNR, and
overhead performance are targeted. This conclusion holds true in various
propagation environments and 3D locations of a mmW pico-cell with up to 140m
radius.Comment: 14 pages, 7 figures, submitted to IEEE Journal of Selected Topics in
Signal Processin
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