53 research outputs found
EM-Based Estimation and Compensation of Phase Noise in Massive-MIMO Uplink Communications
Phase noise (PN) is a major disturbance in MIMO systems, where the
contribution of different oscillators at the transmitter and the receiver side
may degrade the overall performance and offset the gains offered by MIMO
techniques. This is even more crucial in the case of massive MIMO, since the
number of PN sources may increase considerably. In this work, we propose an
iterative receiver based on the application of the expectation-maximization
algorithm. We consider a massive MIMO framework with a general association of
oscillators to antennas, and include other channel disturbances like imperfect
channel state information and Rician block fading. At each receiver iteration,
given the information on the transmitted symbols, steepest descent is used to
estimate the PN samples, with an optimized adaptive step size and a
threshold-based stopping rule. The results obtained for several test cases show
how the bit error rate and mean square error can benefit from the proposed
phase-detection algorithm, even to the point of reaching the same performance
as in the case where no PN is present{\color{black}, offering better results
than a state-of-the-art alternative}. Further analysis of the results allow to
draw some useful trade-offs respecting final performance and consumption of
resources.Comment: Submitted to IEEE Transactions on Communication
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
Updating and Downdating The Linear Decoder for The Uplink of Multiuser Massive MIMO Systems
Deteksi Zero forcing dan MMSE umumnya sering digunakan untuk memperkirakan atau menghitung berapa approximation channel di massive MIMO. Di penelitian syang telah dilakukan sebelumnya banyak menggunakan salah satu detector saja dalam menghitung channel. Di penelitian ini, algoritma yang dibuat dapat dipergunakan untuk kedua tipe detector ini zero forcing atau MMSE ketika salah satu user dating atau pergi dari channel. Penelitian ini berdasarkan algoritma Singular Value Decomposition (SVD) dari matrik channel yang dikembangkan dengan gabungan dari Gram Schmid saat updating (salah satu user datang/masuk channel) dan menggunakan Given Rotation saat downdating. (salah satu user meninggalkan channel) untuk menjaga bentuk matriks dalam SVD.
Kita menggunakan Given rotation untuk membuat matrix menjadi bidiagonal dan Golub Kahan untuk menghilangkan matriks diagonalnya. Hasil penelitian ini mengindikasikan bahwa penelitian ini menghasilkan hasil performansi yang lebih baik dari skela yang lain dengan kompleksitas yang lebih rendah
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
Deep Learning Aided Parametric Channel Covariance Matrix Estimation for Millimeter Wave Hybrid Massive MIMO
Millimeter-wave (mmWave) channels, which occupy frequency ranges much higher
than those being used in previous wireless communications systems, are utilized
to meet the increased throughput requirements that come with 5G communications.
The high levels of attenuation experienced by electromagnetic waves in these
frequencies causes MIMO channels to have high spatial correlation. To attain
desirable error performances, systems require knowledge about the channel
correlations. In this thesis, a deep neural network aided method is proposed
for the parametric estimation of the channel covariance matrix (CCM), which
contains information regarding the channel correlations. When compared to some
methods found in the literature, the proposed method yields satisfactory
performance in terms of both computational complexity and channel estimation
errors.Comment: M.Sc. Thesis, published at:
https://open.metu.edu.tr/handle/11511/9319
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