• ETRI Journal
• /
• v.39 no.3
• /
• pp.326-335
• /
• 2017

In the uplink transmission of massive (or large-scale) multi-input multi-output (MIMO) systems, large dimensional signal detection and its hardware design are challenging issues owing to the high computational complexity. In this paper, we propose low-complexity hardware architectures of Richardson iterative method-based massive MIMO detectors. We present two types of massive MIMO detectors, directly mapped (type1) and reformulated (type2) Richardson iterative methods. In the proposed Richardson method (type2), the matrix-by-matrix multiplications are reformulated to matrix-vector multiplications, thus reducing the computational complexity from $O(U^2)$ to O(U). Both massive MIMO detectors are implemented using a 65 nm CMOS process and compared in terms of detection performance under different channel conditions (high-mobility and flat fading channels). The hardware implementation results confirm that the proposed type1 Richardson method-based detector demonstrates up to 50% power savings over the proposed type2 detector under a flat fading channel. The type2 detector indicates a 37% power savings compared to the type1 under a high-mobility channel.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.10
• /
• pp.4214-4230
• /
• 2020

This paper introduces a pilot allocation scheme for massive MIMO systems based on deep convolutional neural network (CNN) learning. This work is an extension of a prior work on the basic deep learning framework of the pilot assignment problem, the application of which to a high-user density nature is difficult owing to the factorial increase in both input features and output layers. To solve this problem, by adopting the advantages of CNN in learning image data, we design input features that represent users' locations in all the cells as image data with a two-dimensional fixed-size matrix. Furthermore, using a sorting mechanism for applying proper rule, we construct output layers with a linear space complexity according to the number of users. We also develop a theoretical framework for the network capacity model of the massive MIMO systems and apply it to the training process. Finally, we implement the proposed deep CNN-based pilot assignment scheme using a commercial vanilla CNN, which takes into account shift invariant characteristics. Through extensive simulation, we demonstrate that the proposed work realizes about a 98% theoretical upper-bound performance and an elapsed time of 0.842 ms with low complexity in the case of a high-user-density condition.

• ETRI Journal
• /
• v.44 no.6
• /
• pp.885-902
• /
• 2022

The mmWave cell-free massive MIMO (CFmMIMO), combining the advantages of wide bandwidth in the mmWave frequency band and the high- and uniform-spectral efficiency of CFmMIMO, has recently emerged as one of the enabling technologies for 6G. In this paper, we propose a novel framework for energy-efficient mmWave CFmMIMO systems that uses low-resolution digital-analog converters (DACs) and phase shifters (PSs) to introduce lowcomplexity hybrid precoding. Additionally, we propose a heuristic pilot allocation scheme that makes the best effort to slash some interference from copilot users. The simulation results show that the proposed hybrid precoding and pilot allocation scheme outperforms the existing schemes. Furthermore, we reveal the relationship between the energy and spectral efficiencies for the proposed mmWave CFmMIMO system by modeling the whole network power consumption and observe that the introduction of low-resolution DACs and PSs is effective in increasing the energy efficiency by compromising the spectral efficiency and the network power consumption.

• ETRI Journal
• /
• v.38 no.2
• /
• pp.280-290
• /
• 2016

To implement high-order multiuser multiple input and multiple output (MU-MIMO) for massive MIMO systems, there must be a feedback scheme that can warrant its performance with a limited signaling overhead. The interference-to-noise ratio can be a basis for a novel form of Codebook (CB)-based MU-MIMO feedback scheme. The objective of this paper is to verify such a scheme's performance under a practical system configuration with a 3D channel model in various radio environments. We evaluate the performance of various CB-based feedback schemes with different types of overhead reduction approaches, providing an experimental ground with which to optimize a CB-based MU-MIMO feedback scheme while identifying the design constraints for a massive MIMO system.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38A no.8
• /
• pp.724-732
• /
• 2013

In this paper, we analyze a performance of multiuser massive multiple-input and multiple-output (MIMO) system. We derive the ergodic cell capacity based on a downlink frame structure and analyze the ergodic cell capacity with respect to the number of base station (BS) antennas and the number of users. This paper shows that the ergodic cell capacity is a concave function with respect to the number of BS antennas and the number of users, and also derives the optimal numbers of BS antennas and users for the maximum cell capacity. The simulation results verify the derived analyses and show that the derived numbers of BS antennas and users provide the maximum cell capacity.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.40 no.8
• /
• pp.1485-1491
• /
• 2015

This paper introduces a new pilot assignment algorithm for uplink Massive multiple-input multiple-output (MIMO) systems. Since the conventional pilot assignment algorithm has the performance degradation compared to the optimal algorithm which performs the exhaustive search, we propose a new pilot assignment algorithm using Pre-determined Interference and Pre-determined Desired-term techniques. The proposed algorithm has the low complexity and guarantees negligible performance loss compared to the optimal algorithm. Simulation result verifies that the proposed algorithm achieves a large performance gain over the conventional algorithm.

• Journal of Communications and Networks
• /
• v.15 no.4
• /
• pp.398-406
• /
• 2013

In this paper, we investigate two types of in-phase and quadrature-phase (IQ) data transfer methods for cloud multiple-input multiple-output (MIMO) network operation. They are termed "after-precoding" and "before-precoding". We formulate a cloud massive MIMO operation problem that aims at selecting the best IQ data transfer method and transmission strategy (beamforming technique, the number of concurrently receiving users, the number of used antennas for transmission) to maximize the ergodic sum-rate under a limited capacity of the digital unit-radio unit link. Based on our proposed solution, the optimal numbers of users and antennas are simultaneously chosen. Numerical results confirm that the sum-rate gain is greater when adaptive "after/before-precoding" method is available than when only conventional "after-precoding" IQ-data transfer is available.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.40 no.7
• /
• pp.1239-1247
• /
• 2015

In frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) system, the computational complexity of downlink channel estimation is proportional to the number of antennas at a base station. Therefore, effective channel estimation techniques may have to be studied. In this paper, novel channel estimation algorithms using adaptive techniques such as Kalman and least mean square (LMS) filters are proposed in a channel model with temporal and spatial correlation.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.1
• /
• pp.237-253
• /
• 2019

For massive multiple-input multiple-output (MIMO) circumstances with time division duplex (TDD) protocol, pilot contamination becomes one of main system performance bottlenecks. This paper proposes an uplink pilot sequence assignment to alleviate this problem for spatially correlated massive MIMO circumstances. Firstly, a single-cell TDD massive MIMO model with multiple terminals in the cell is established. Then a spatial correlation between two channel response vectors is established by the large-scale fading variables and the angle of arrival (AOA) span with an infinite number of base station (BS) antennas. With this spatially correlated channel model, the expression for the achievable system capacity is derived. To optimize the achievable system capacity, a problem regarding uplink pilot assignment is proposed. In view of the exponential complexity of the exhaustive search approach, a pilot assignment algorithm corresponding to the distinct channel AOA intervals is proposed to approach the optimization solution. In addition, simulation results prove that the main pilot assignment algorithm in this paper can obtain a noticeable performance gain with limited BS antennas.

• ETRI Journal
• /
• v.42 no.1
• /
• pp.26-35
• /
• 2020

Herein, we consider uplink multiuser massive multiple-input multiple-output systems when multiple users transmit information symbols to a base station (BS) by applying simple space-time block coding (STBC). At the BS receiver, two detection filters for each user are used to detect the STBC information symbols. One of these filters is for odd-indexed symbols and the other for even-indexed symbols. Using constrained output variance metric minimization, we first derive a special relation between the closed-form optimal solutions for the two detection filters. Then, using the derived special relation, we propose a new blind adaptive algorithm for implementing the minimum output variance-based optimal filters. In the proposed adaptive algorithm, filter weight vectors are updated only in the region satisfying the special relation. Through a theoretical analysis of the convergence speed and a computer simulation, we demonstrate that the proposed scheme exhibits faster convergence speed and lower steady-state bit error rate than the conventional scheme.