A Simple Toeplitz Channel Matrix Decomposition with Vectorization Technique for Large scaled MIMO System

Author(s):  
Ju Yong Park ◽  
Mohammad Abu Hanif ◽  
Jeong Su Kim ◽  
Sang Seob Song ◽  
Moon Ho Lee
2014 ◽  
Vol 13 ◽  
pp. 1509-1512 ◽  
Author(s):  
Bo Tang ◽  
Kun-Yi Guo ◽  
Jian-Ping Wang ◽  
Xin-Qing Sheng ◽  
Xian-Wei Zhou
Keyword(s):  

2018 ◽  
Vol 8 (10) ◽  
pp. 1747 ◽  
Author(s):  
Won-Chang Kim ◽  
Min-Jae Paek ◽  
Hyoung-Kyu Song

Recently, wireless communication systems use a multi-hop transmission scheme using a relay to expand the cell coverage of the system. The multi-hop transmission scheme can expand the cell coverage of wireless communication systems. However, if an appropriate relay is not selected, errors generated during signal processing in the relay are transmitted to the receiver. Therefore, a relay selection scheme is essential for reliable multi-hop transmission. This paper proposes a relay selection scheme for reliable multi-hop transmission in a multi user-multiple input multiple output (MU-MIMO) system. The proposed relay selection scheme uses a MIMO channel matrix between the transmitter and relays to select an appropriate relay. The proposed relay selection scheme obtains the singular values of the MIMO channel matrix using the singular value decomposition (SVD). Then, the sum of the singular values is calculated, and the relay having the largest value is selected. Therefore, in the proposed relay selection scheme, although the transmitter only knows the channel information between the transmitter and relays, the transmitter can select an appropriate relay for reliable multi-hop transmission.


Author(s):  
Tadashi Fujino

This paper proposes an improved lattice-reduction aided (LRA) MMSE detection scheme, based on the Gram-Schmidt (GS) procedure. The proposed scheme reduces the column vectors of the MIMO channel matrix, by using the LLL algorithm followed by the GS procedure in order to transform the channel matrix into a new one which has mutually purely orthogonal column vectors. Compared to the conventional LRA MMSE detector, the proposed detector achieves a very good BER performance, almost equivalent to those using the ML detector in the 4 × 4 MIMO system at the cost of a slightly larger computational complexity.


2017 ◽  
Vol 2017 ◽  
pp. 1-9
Author(s):  
Dazhi Piao ◽  
Xingning Jia ◽  
Xiaochuan Ma ◽  
Qingxin Guo ◽  
Zengrui Li

A spatial multiplexing (SM) array and a beamforming (BF) array with similar antenna size working at 28 GHz are designed and fabricated. In the SM array, a 4 × 4 MIMO system is realized with each port composed of a four-element subarray. In the BF array, the whole 16 elements are used to formulate a high-gain array. The measured S-parameters are in agreement with the simulated results. For both arrays, the channel capacities are computed by the measured channel matrix and signal-to-noise ratio (SNR) in an office room. Results show that capacity of the SM system is larger than that of the BF system, although the gain of BF array is about 5 dB larger than that of the SM array. However, the capacity of the SM array depends heavily on SNR; specifically, for the 1 dBm transmit power, communication distance R=25 cm, the ergodic capacity of the SM system is 2.76 times that of the BF system, and if R=250 cm, the capacity gain is reduced to 1.45. Furthermore, compared with the BF array, the SM array has a more robust performance over antenna misalignment, because of the wider beamwidth.


2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Gao Xi jun ◽  
Chen Zi li ◽  
Hu Yong Jiang

Based on the three-dimensional GBSBCM (geometrically based double bounce cylinder model) channel model of MIMO for unmanned aerial vehicle (UAV), the simple form of UAV space-time-frequency channel correlation function which includes the LOS, SPE, and DIF components is presented. By the methods of channel matrix decomposition and coefficient normalization, the analytic formula of UAV-MIMO normalized correlation matrix is deduced. This formula can be used directly to analyze the condition number of UAV-MIMO channel matrix, the channel capacity, and other characteristic parameters. The simulation results show that this channel correlation matrix can be applied to describe the changes of UAV-MIMO channel characteristics under different parameter settings comprehensively. This analysis method provides a theoretical basis for improving the transmission performance of UAV-MIMO channel. The development of MIMO technology shows practical application value in the field of UAV communication.


2021 ◽  
Author(s):  
◽  
Callum Thomas Neil

<p>A novel technical solution, and paradigm shift, envisioned to achieve the significant spectral efficiency enhancements required for Fifth Generation (5G) wireless systems is massive multiple-input-multiple-output (MIMO). Massive MIMO systems scale up the number of transmit (TX) and receive (RX) antennas by at least an order of magnitude relative to conventional multi-user MIMO systems, which have been a key feature in current wireless standards, such as Long Term Evolution. Thus, massive MIMO leverages the spatial dimension by providing significant increases in all the virtues of conventional MIMO systems but on a much larger scale. Namely, data rate, link reliability, energy efficiency, and multiplexing gains can all be increased with massive MIMO systems, while simultaneously reducing inter-user interference through digital processing techniques. Further motivating the surge in research of massive MIMO systems are the additional channel properties which occur when operating with large dimensions. These properties arise as a result of random matrix theory asymptotics and under these conditions random variables become deterministic, simplifying analysis and allowing simple processing techniques to become (near) optimal. These idealistic properties, however, are based on the assumptions of an independent and identically distributed channel matrix with an infinite number of TX antennas.  Physical contraints typically prohibit the deployment of large numbers of TX antennas. It therefore seems natural to determine the number of TX antennas required for large MIMO systems to begin to exhibit these favourable asymptotic properties. Analytically deriving the first and second moments of the composite Wishart channel matrix and numerically defining three convergence metrics, the rate of channel convergence is examined. Limiting matched-filter (MF) and zero-forcing precoding signal-to-interference-plus-noise-ratio (SINR) performances are then analytically derived and rate of convergence shown. Coordinated distributed MIMO systems can mitigate the detrimental effects of spatial correlation relative to a colocated MIMO system. The instantaneous and limiting MF SINR performance of a distributed massive MIMO system is derived, allowing clear insights into the effects of imperfect channel state information, spatial correlation, link gains and number of antenna clusters. The wide bandwidths vacant at millimeter-wave (mmWave) frequency bands are suitable for 5G wireless systems since they occupy regions of uncongested spectrum which enable large contiguous bandwidth carriers. Spatial correlation of an arbitrary antenna array topology is analytically derived for a mmWave channel model. Numerically, the effects of mutual coupling amongst antenna elements is then shown on the effective spatial correlation, eigenvalue structure and user rate of different antenna topologies.   Channel models and measurements across a wide range of candidate bands for 5G wireless systems are then considered, motivated by the different propagation and spatial characteristics between different bands and different channel models within the same band. Key channel modelling and spatial parameter differences are identified and, in turn, their impact on various antenna topologies investigated, in terms of system sum rate, channel eigenvalue structure, effective degrees of freedom and massive MIMO convergence properties.</p>


Author(s):  
Thanh-Binh Nguyen ◽  
Minh-Tuan Le ◽  
Vu-Duc Ngo ◽  
Tien-Dong Nguyen ◽  
Huy-Dung Han

In Multiple Input Multiple Output (MIMO) systems, the complexities of detectors depend on the size of the channel matrix. In Massive MIMO systems, detection complexity becomes remarkably higher because the dimensions of the channel matrix get much larger. In order to recover the signals in the up-link of a Massive MIMO system at reduced complexities, we first divide the system into two sub-systems. After that, we apply the Minimum Mean Square Error (MMSE) and Bell Laboratory Layer Space Time (BLAST) detectors to each subsystem, resulting in the so-called MMSE-GD and BLAST-GD detectors, respectively. To further enhance the BER performance of Massive MIMO systems under the high-load conditions, we propose two additional detectors, called MMSE-IGD and BLAST-IGD by respectively applying the conventional MMSE and BLAST on the sub-systems in an iterative manner. It is shown via computer simulation and analytical results that the proposed detectors enable the system to achieve not only higher BER performance but also low detection complexities as compared to the conventional linear detectors. Moreover, the MMSE-IGD and BLAST-IGD can significantly improve BER performance of Massive MIMO systems.


Sign in / Sign up

Export Citation Format

Share Document