66 research outputs found
Gaussian Message Passing for Overloaded Massive MIMO-NOMA
This paper considers a low-complexity Gaussian Message Passing (GMP) scheme
for a coded massive Multiple-Input Multiple-Output (MIMO) systems with
Non-Orthogonal Multiple Access (massive MIMO-NOMA), in which a base station
with antennas serves sources simultaneously in the same frequency.
Both and are large numbers, and we consider the overloaded cases
with . The GMP for MIMO-NOMA is a message passing algorithm operating
on a fully-connected loopy factor graph, which is well understood to fail to
converge due to the correlation problem. In this paper, we utilize the
large-scale property of the system to simplify the convergence analysis of the
GMP under the overloaded condition. First, we prove that the \emph{variances}
of the GMP definitely converge to the mean square error (MSE) of Linear Minimum
Mean Square Error (LMMSE) multi-user detection. Secondly, the \emph{means} of
the traditional GMP will fail to converge when . Therefore, we propose and derive a new
convergent GMP called scale-and-add GMP (SA-GMP), which always converges to the
LMMSE multi-user detection performance for any , and show that it
has a faster convergence speed than the traditional GMP with the same
complexity. Finally, numerical results are provided to verify the validity and
accuracy of the theoretical results presented.Comment: Accepted by IEEE TWC, 16 pages, 11 figure
Improved Two-Dimensional Double Successive Projection Algorithm for Massive MIMO Detection
In a massive MIMO system, a large number of receiving antennas at the base station can simultaneously serve multiple users. Linear detectors can achieve optimal performance but require large dimensional matrix inversion, which requires a large number of arithmetic operations. Several low complexity solutions are reported in the literature. In this work, we have presented an improved two-dimensional double successive projection (I2D-DSP) algorithm for massive MIMO detection. Simulation results show that the proposed detector performs better than the conventional 2D-DSP algorithm at a lower complexity. The performance under channel correlation also improves with the I2D-DSP scheme. We further developed a soft information generation algorithm to reduce the number of magnitude comparisons. The proposed soft symbol generation method uses real domain operation and can reduce almost 90% flops and magnitude comparisons
A tutorial on the characterisation and modelling of low layer functional splits for flexible radio access networks in 5G and beyond
The centralization of baseband (BB) functions in a radio access network (RAN) towards data processing centres is receiving increasing interest as it enables the exploitation of resource pooling and statistical multiplexing gains among multiple cells, facilitates the introduction of collaborative techniques for different functions (e.g., interference coordination), and more efficiently handles the complex requirements of advanced features of the fifth generation (5G) new radio (NR) physical layer, such as the use of massive multiple input multiple output (MIMO). However, deciding the functional split (i.e., which BB functions are kept close to the radio units and which BB functions are centralized) embraces a trade-off between the centralization benefits and the fronthaul costs for carrying data between distributed antennas and data processing centres. Substantial research efforts have been made in standardization fora, research projects and studies to resolve this trade-off, which becomes more complicated when the choice of functional splits is dynamically achieved depending on the current conditions in the RAN. This paper presents a comprehensive tutorial on the characterisation, modelling and assessment of functional splits in a flexible RAN to establish a solid basis for the future development of algorithmic solutions of dynamic functional split optimisation in 5G and beyond systems. First, the paper explores the functional split approaches considered by different industrial fora, analysing their equivalences and differences in terminology. Second, the paper presents a harmonized analysis of the different BB functions at the physical layer and associated algorithmic solutions presented in the literature, assessing both the computational complexity and the associated performance. Based on this analysis, the paper presents a model for assessing the computational requirements and fronthaul bandwidth requirements of different functional splits. Last, the model is used to derive illustrative results that identify the major trade-offs that arise when selecting a functional split and the key elements that impact the requirements.This work has been partially funded by Huawei Technologies. Work by X. Gelabert and B. Klaiqi is partially funded by the European Union's Horizon Europe research and innovation programme (HORIZON-MSCA-2021-DN-0) under the Marie Skłodowska-Curie grant agreement No 101073265. Work by J. Perez-Romero and O. Sallent is also partially funded by the Smart Networks and Services Joint Undertaking (SNS JU) under the European Union’s Horizon Europe research and innovation programme under Grant Agreements No. 101096034 (VERGE project) and No. 101097083 (BeGREEN project) and by the Spanish Ministry of Science and Innovation MCIN/AEI/10.13039/501100011033 under ARTIST project (ref. PID2020-115104RB-I00). This last project has also funded the work by D. Campoy.Peer ReviewedPostprint (author's final draft
Alternative Normalized-Preconditioning for Scalable Iterative Large-MIMO Detection
Signal detection in large multiple-input multiple-output (large-MIMO) systems
presents greater challenges compared to conventional massive-MIMO for two
primary reasons. First, large-MIMO systems lack favorable propagation
conditions as they do not require a substantially greater number of service
antennas relative to user antennas. Second, the wireless channel may exhibit
spatial non-stationarity when an extremely large aperture array (ELAA) is
deployed in a large-MIMO system. In this paper, we propose a scalable iterative
large-MIMO detector named ANPID, which simultaneously delivers 1) close to
maximum-likelihood detection performance, 2) low computational-complexity
(i.e., square-order of transmit antennas), 3) fast convergence, and 4)
robustness to the spatial non-stationarity in ELAA channels. ANPID incorporates
a damping demodulation step into stationary iterative (SI) methods and
alternates between two distinct demodulated SI methods. Simulation results
demonstrate that ANPID fulfills all the four features concurrently and
outperforms existing low-complexity MIMO detectors, especially in highly-loaded
large MIMO systems.Comment: Accepted by IEEE GLOBECOM 202
Efficient low-complexity data detection for multiple-input multiple-output wireless communication systems
The tradeoff between the computational complexity and system performance in multipleinput
multiple-output (MIMO) wireless communication systems is critical to practical applications.
In this dissertation, we investigate efficient low-complexity data detection schemes
from conventional small-scale to recent large-scale MIMO systems, with the targeted applications
in terrestrial wireless communication systems, vehicular networks, and underwater
acoustic communication systems.
In the small-scale MIMO scenario, we study turbo equalization schemes for multipleinput
multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) and multipleinput
multiple-output single-carrier frequency division multiple access (MIMO SC-FDMA)
systems. For the MIMO-OFDM system, we propose a soft-input soft-output sorted QR decomposition
(SQRD) based turbo equalization scheme under imperfect channel estimation.
We demonstrate the performance enhancement of the proposed scheme over the conventional
minimum mean-square error (MMSE) based turbo equalization scheme in terms of
system bit error rate (BER) and convergence performance. Furthermore, by jointly considering
channel estimation error and the a priori information from the channel decoder,
we develop low-complexity turbo equalization schemes conditioned on channel estimate for
MIMO systems. Our proposed methods generalize the expressions used for MMSE and
MMSE-SQRD based turbo equalizers, where the existing methods can be viewed as special
cases. In addition, we extend the SQRD-based soft interference cancelation scheme
to MIMO SC-FDMA systems where a multi-user MIMO scenario is considered. We show
an improved system BER performance of the proposed turbo detection scheme over the
conventional MMSE-based detection scheme.
In the large-scale MIMO scenario, we focus on low-complexity detection schemes because
computational complexity becomes critical issue for massive MIMO applications. We first propose an innovative approach of using the stair matrix in the development of massive
MIMO detection schemes. We demonstrate the applicability of the stair matrix through
the study of the convergence conditions. We then investigate the system performance and
demonstrate that the convergence rate and the system BER are much improved over the
diagonal matrix based approach with the same system configuration. We further investigate
low-complexity and fast processing detection schemes for massive MIMO systems where a
block diagonal matrix is utilized in the development. Using a parallel processing structure,
the processing time can be much reduced. We investigate the convergence performance
through both the probability that the convergence conditions are satisfied and the convergence
rate, and evaluate the system performance in terms of computational complexity,
system BER, and the overall processing time. Using our proposed approach, we extend
the block Gauss-Seidel method to large-scale array signal detection in underwater acoustic
(UWA) communications. By utilizing a recently proposed computational efficient statistic
UWA channel model, we show that the proposed scheme can effectively approach the system
performance of the original Gauss-Seidel method, but with much reduced processing delay
Fast matrix inversion based on Chebyshev acceleration for linear detection in massive MIMO systems
MASSIVE5G (SAICT‐45‐2017‐02)To circumvent the prohibitive complexity of linear minimum mean square error detection in a massive multiple-input multiple-output system, several iterative methods have been proposed. However, they can still be too complex and/or lead to non-negligible performance degradation. In this letter, a Chebyshev acceleration technique is proposed to overcome the limitations of iterative methods, accelerating the convergence rates and enhancing the performance. The Chebyshev acceleration method employs a new vector combination, which combines the spectral radius of the iteration matrix with the receiver signal, and also the optimal parameters of Chebyshev acceleration have also been defined. A detector based on iterative algorithms requires pre-processing and initialisation, which enhance the convergence, performance, and complexity. To influence the initialisation, the stair matrix has been proposed as the first start of iterative methods. The performance results show that the proposed technique outperforms state-of-the-art methods in terms of error rate performance, while significantly reducing the computational complexity.publishersversionpublishe
Massive MIMO Systems With Low-Resolution ADCs: Baseband Energy Consumption vs. Symbol Detection Performance
In massive multiple-input multiple-output (MIMO) systems using a large number of antennas, it would be difficult to connect high-resolution analog-to-digital converters (ADCs) to each antenna component due to high cost and energy consumption problems. To resolve these issues, there has been much work on implementing symbol detectors and channel estimators using low-resolution ADCs for massive MIMO systems. Although it is intuitively true that using low-resolution ADCs makes it possible to save a large amount of energy consumption in massive MIMO systems, the relationship between energy consumption using low-resolution ADCs and detection performance has not been properly analyzed yet. In this paper, the tradeoff between different detectors and total baseband energy consumption including flexible ADCs is thoroughly analyzed taking the optimal fixed-point operations performed during the detection processes into account. In order to minimize the energy consumption for the given channel condition, the proposed scheme selects the best mode among various processing options while supporting the target frame error rate. The numerous case studies reveal that the proposed work remarkably saves the energy consumption of the massive MIMO processing compared with the existing schemes.11Ysciescopu
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