scholarly journals Frequency-Selective Wallpaper for Indoor Interference Reduction and MIMO Capacity Improvement

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 695
Author(s):  
José-Víctor Rodríguez ◽  
Mats Gustafsson ◽  
José-María Molina-García-Pardo ◽  
Leandro Juan-Llácer ◽  
Ignacio Rodríguez-Rodríguez
Keyword(s):  
Mimo Systems ◽  
Radio Channel ◽  
Multiple Input Multiple Output ◽  
Singular Values ◽  
Indoor Environments ◽  
Radio Communication ◽  
Single Input Single Output ◽  
Communication Signals ◽  
Frequency Selective ◽  
5 Ghz

This paper presents the design and features of frequency-selective wallpaper—based on periodic and symmetric metallic hexagons—intended to be attached to standard walls for filtering out 5 GHz signals (e.g., IEEE 802.11a systems) without blocking other selected radio communication services (e.g., cellular mobile communication signals). It analyzes the characteristics of the radio channel—as found within standard indoor environments—with both regular walls and walls with the proposed frequency-selective wallpaper, examined using a ray-launching program for single-input single-output (SISO) and multiple-input multiple-output (MIMO) systems. This allows the harvesting of parameters, including channel capacity, power delay profile, and signal-to-interference ratio, for proper comparison between the two environments under study: with and without the presented wallpaper. The achieved results clearly show that the use of the proposed frequency-selective wallpaper in an indoor scenario reduces interference levels by an additional attenuation of up to 20 dB in comparison to an unpapered wall. Additionally, with MIMO systems, radio channel characteristics, such as capacity, are improved due to the increase in the magnitude of all singular values of the channel transfer matrix compared to the unpapered wall case, thereby leading to the existence of more relevant subchannels.

scholarly journals Block Method for SolvingState-Space Equations of Linear Continuous-Time Control Systems

2007 ◽  
Vol 4 (2) ◽  
pp. 318-329
Author(s):  
Baghdad Science Journal
Keyword(s):  
State Space ◽  
Continuous Time ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Method Comparison ◽  
The State ◽  
Block Method ◽  
Single Input Single Output ◽  
Single Output ◽  
Time Control

This paper presents a newly developed method with new algorithms to find the numerical solution of nth-order state-space equations (SSE) of linear continuous-time control system by using block method. The algorithms have been written in Matlab language. The state-space equation is the modern representation to the analysis of continuous-time system. It was treated numerically to the single-input-single-output (SISO) systems as well as multiple-input-multiple-output (MIMO) systems by using fourth-order-six-steps block method. We show that it is possible to find the output values of the state-space method using block method. Comparison between the numerical and exact results has been given for some numerical examples for solving different types of state-space equations using block method for conciliated the accuracy of the results of this method.

Adaptive Aperture Aided Antenna Design for SISO-MIMO Systems using Fuzzy C-Mean Clustering

2015 ◽  
Vol 4 (3) ◽  
pp. 33-47
Author(s):  
Parismita A Kashyap ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Mimo Systems ◽  
High Reliability ◽  
Multiple Input Multiple Output ◽  
Multipath Fading ◽  
Time Instant ◽  
Antenna Structure ◽  
Single Input Single Output ◽  
Single Time ◽  
Effective Aperture ◽  
Transmitting Antenna

One of the most relevant themes of wireless communication is to achieve better spectral efficiency and provide high reliability while providing rich-content data services despite the existence of several serious challenges. A few of them are multipath fading, multi-user interference, co-channel interference (CCI), inter symbol interference (ISI) etc to name a few. Several techniques have already been developed and deployed to eliminate the fading effects. One of the less explored techniques which have been adopted and discussed in this chapter is based on the structure of the transmitting antenna. The physical dimension of the antenna is varied as per the fading condition by adopting a dynamic process which adjusts the structure to provide the best quality of service (QoS). Two types of antenna set-ups are considered - Single Input-Single Output (SISO) and Multiple Input-Multiple Output (MIMO). The transmitting antenna in this system adaptively updates its aperture to improve the system performance and at the same time optimizes the driving power of the antenna as per requirement. The system changes the effective aperture of the transmitting antenna in high data rate, time varying Rayleigh channels to adapt to a previously set Bit error Rate (BER). However, in a real time environment the BER keeps on changing based on the channel condition. It is difficult to attain a fixed value of BER and hence even more difficult to model the antenna structure for a single time instant. As a result there exist a number of effective aperture dimensions for various BER in a single time instant. Out of the various values, two specific limits of the effective aperture of the transmitting antenna needs to be decided. Fuzzy C-Mean (FCM) Clustering method being one of the most popular and efficient clustering technique is used to set two limits of the aperture within which a particular threshold of the BER is obtained at one particular instant of time. The results derived show the effectiveness of the entire system.

scholarly journals A Semiempirical MIMO Channel Model in Obstructed Viaduct Scenarios on High-Speed Railway

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Tao Zhou ◽  
Cheng Tao ◽  
Liu Liu ◽  
Zhenhui Tan
Keyword(s):  
High Speed ◽  
Channel Model ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Time Correlation ◽  
Time Correlation Function ◽  
Mimo Channel ◽  
High Speed Railway ◽  
Single Input Single Output ◽  
Single Output

A semiempirical multiple-input multiple-output (MIMO) channel model is proposed for high-speed railway (HSR) viaduct scenarios. The proposed MIMO model is based on the combination of realistic single-input single-output (SISO) channel measurement results and a theoretical geometry-based stochastic model (GBSM). Temporal fading characteristics involvingK-factor and Doppler power spectral density (PSD) are derived from the wideband measurement under an obstructed viaduct on Zhengzhou-Xi’an HSR in China. The GBSM composed of a one-ring model and an elliptical model is employed to describe the entire propagation environment. Environment-related parameters in the GBSM are determined by the measured temporal fading properties. And a close agreement is achieved between the model results and measured data. Finally, a deterministic simulation model is established to perform the analysis of the space-time correlation function, the space-Doppler PSD, and the channel capacity for the measured scenario. This model is more realistic and particularly beneficial for the performance evaluation of MIMO systems in HSR environments.

scholarly journals Synchronization in MIMO OFDM systems

2005 ◽  
Vol 2 ◽  
pp. 147-153 ◽  
Author(s):  
L. Häring ◽  
A. Czylwik
Keyword(s):  
Carrier Frequency ◽  
Orthogonal Frequency Division Multiplexing ◽  
Carrier Frequency Offset ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
High Sensitivity ◽  
Ofdm Systems ◽  
Single Input Single Output ◽  
Input Multiple Output ◽  
Single Input

Abstract. In this paper, an overview of carrier frequency offset (CFO) estimation algorithms for Orthogonal Frequency Division Multiplexing (OFDM) systems is presented. It is well-known that multicarrier systems suffer from their high sensitivity to mismatches of transmitter and receiver oscillator frequencies. The performance degrades since the CFO destroys the orthogonality of the subcarriers. Hence, extensive research has been done on the estimation and correction of the CFO in Single-Input Single-Output (SISO) systems. Mainly, the proposed algorithms can be categorized into data-aided and blind techniques. Several estimation techniques have been extended to the Single-Input Multiple- Output (SIMO) case where multiple receive antennas can be utilized to gain diversity. However, less attention has been paid on synchronization in the attractive Multiple-Input Multiple-Output (MIMO) case which is topic of tremendous interest in current research. The present paper concentrates on aspects of this new scenario. Starting with algorithms for SISO and SIMO, this contribution reviews briefly proposed carrier frequency synchronization techniques which could be implemented in forthcoming MIMO systems.

scholarly journals Effective Power Allocation Using for Transmit Source In MIMO Relay Communication

2021 ◽  
Vol 5 (3) ◽  
Author(s):  
Hoai Trung Tran
Keyword(s):  
Channel Capacity ◽  
Communication Systems ◽  
High Speed ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Optimization Method ◽  
Radio Communication ◽  
Second Derivatives ◽  
Multiple Input ◽  
Input Multiple Output

The Multiple Input Multiple Output (MIMO) systems using relays are of interest for high-speed radio communication systems. Currently, most of the articles are interested in the model of three nodes with purposes such as increasing the channel capacities (mutual information) or reducing the minimum mean square of error. This paper extends to more than one relay and is concerned with the maximum channel capacity. It is assumed that the channel matrices between source and relay as well as relay and receiver are random matrices; the relay precoders are also assumed to be random and known at the receiver. The article proposes that the Lagrange multiplier finding algorithm using the Newton – Raphson optimization method is more straightforward than the traditional finding algorithm using the first and second derivatives but still gives a higher channel capacity.

scholarly journals Deep Learning-based Cooperative Automatic Modulation Classification Method for MIMO Systems

2020 ◽  
Author(s):  
Yu Wang ◽  
Juan Wang ◽  
Jie Yang ◽  
Wei Zhang ◽  
Guan Gui
Keyword(s):  
Deep Learning ◽  
Communication Systems ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Decision Rules ◽  
Modulation Classification ◽  
Single Input Single Output ◽  
Automatic Modulation Classification ◽  
Single Output ◽  
Input Multiple Output

Automatic modulation classification (AMC) is one of the most essential algorithms to identify the modulation types for the non-cooperative communication systems. Recently, it has been demonstrated that deep learning (DL)-based AMC method effectively works in the single-input single-output (SISO) systems, but DL-based AMC method is scarcely explored in the multiple-input multiple-output (MIMO) systems. In this correspondence, we propose a convolutional neural network (CNN)-based cooperative AMC (Co-AMC) method for the MIMO systems, where the receiver equipped with multiple antennas cooperatively recognizes the modulation types. Specifically, each received antenna gives their recognition sub-results via the CNN, respectively. Then, the decision maker identifies the modulation types with the recognition sub-results and cooperative decision rules, such as direct voting (DV), weighty voting (WV), direct averaging (DA) and weighty averaging (WA). The simulation results demonstrate that the Co-AMC method, based on the CNN and WA, has the highest correct classification probability in the four cooperative decision rules. In addition, the CNN-based Co-AMC method also performs better than the high order cumulants (HOC)-based traditional AMC methods, which shows the effective feature extraction and powerful classification capabilities of the CNN.

scholarly journals LoS Theoretical and Experimental MIMO Study from 1–40 GHz in Indoor Environments

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1688
Author(s):  
Juan Pascual-García ◽  
Maria-Teresa Martinez-Ingles ◽  
Davy P. Gaillot ◽  
Leandro Juan-Llácer ◽  
Jose-Maria Molina-Garcia-Pardo
Keyword(s):  
Radio Channel ◽  
Multiple Input Multiple Output ◽  
Great Accuracy ◽  
Delay Spread ◽  
Indoor Environments ◽  
Received Power ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Indoor Scenarios ◽  
Tracing Techniques

In this work, line-of-sight multiple-input multiple-output (MIMO) measurements in the frequency range from 1 GHz to 40 GHz are presented for an indoor environment in the scope of new 5G bands. For the sake of comparison, the measured radio channels are reproduced with great accuracy using ray-tracing techniques by tuning all propagation mechanisms. The relative received power, root mean square of the delay spread (RMS DS) and K-factor provide an insight of how propagation behaves in indoor scenarios within a large and contiguous frequency band. Results show that a decay factor behaves nearly constant with frequency and the RMS DS is quite sensible to frequency. From these results, faithful one-slope 5G models are proposed. Finally, the contribution of the simulated propagation mechanisms to the radio channel is investigated which suggests that the simulation of the low-mmW radio channel can be simplified.

scholarly journals Optimized Scheduling Technique of Null Subcarriers for Peak Power Control in 3GPP LTE Downlink

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Soobum Cho ◽  
Sang Kyu Park
Keyword(s):  
Power Efficiency ◽  
Multiple Access ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Average Power ◽  
Papr Reduction ◽  
Single Input Single Output ◽  
Single Output ◽  
Multiple Input ◽  
Input Multiple Output

Orthogonal frequency division multiple access (OFDMA) is a key multiple access technique for the long term evolution (LTE) downlink. However, high peak-to-average power ratio (PAPR) can cause the degradation of power efficiency. The well-known PAPR reduction technique, dummy sequence insertion (DSI), can be a realistic solution because of its structural simplicity. However, the large usage of subcarriers for the dummy sequences may decrease the transmitted data rate in the DSI scheme. In this paper, a novel DSI scheme is applied to the LTE system. Firstly, we obtain the null subcarriers in single-input single-output (SISO) and multiple-input multiple-output (MIMO) systems, respectively; then, optimized dummy sequences are inserted into the obtained null subcarrier. Simulation results show that Walsh-Hadamard transform (WHT) sequence is the best for the dummy sequence and the ratio of 16 to 20 for the WHT and randomly generated sequences has the maximum PAPR reduction performance. The number of near optimal iteration is derived to prevent exhausted iterations. It is also shown that there is no bit error rate (BER) degradation with the proposed technique in LTE downlink system.

scholarly journals Linear detectors and precoding methods for massive MIMO

2021 ◽  
Vol 42 (2) ◽  
pp. 209
Author(s):  
Jean Marcel Faria Tonin ◽  
Taufik Abrao
Keyword(s):  
Massive Mimo ◽  
Communication Systems ◽  
Mean Squared Error ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Wireless Channel ◽  
Single Input Single Output ◽  
Single Output ◽  
Minimum Mean Squared Error ◽  
Input Multiple Output

Detection in multiple-input-multiple-output (MIMO) wireless communication systems is a crucial procedure in receivers since the multiple access transmission schemes generate interference due to the simultaneous transmission along with the several antennas, unlike single-input-single-output (SISO) transmission schemes. Precoding is a technique in MIMO systems used to mitigate the effects of the channel over the received signal. Hence, it is possible to adjust continuously the transmitted information to reverse the effect of the wireless channel at the receiver side. In this work, linear sub-optimal detectors and precoders for massive MIMO (M-MIMO) systems are implemented, analyzed, and compared in terms of performance-complexity trade-off. It is also being considered numerical results in both channel scenarios: a) receiver and transmitter have perfect channel state information (CSI); b) complex channel coefficients are estimated with different levels of inaccuracy. Monte-Carlo simulations (MCS) reveal that linear zero-forcing (ZF) and minimum mean squared error (MMSE) massive MIMO detectors result in a certain robustness against multi-user interference when operating under low and medium system loading, L = K/M, thanks to the favourable propagation phenomenon arising in massive MIMO systems.

scholarly journals Deep Learning-based Cooperative Automatic Modulation Classification Method for MIMO Systems

2020 ◽  
Author(s):  
Yu Wang ◽  
Juan Wang ◽  
Jie Yang ◽  
Wei Zhang ◽  
Guan Gui
Keyword(s):  
Deep Learning ◽  
Communication Systems ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Decision Rules ◽  
Modulation Classification ◽  
Single Input Single Output ◽  
Automatic Modulation Classification ◽  
Single Output ◽  
Input Multiple Output

Automatic modulation classification (AMC) is one of the most essential algorithms to identify the modulation types for the non-cooperative communication systems. Recently, it has been demonstrated that deep learning (DL)-based AMC method effectively works in the single-input single-output (SISO) systems, but DL-based AMC method is scarcely explored in the multiple-input multiple-output (MIMO) systems. In this correspondence, we propose a convolutional neural network (CNN)-based cooperative AMC (Co-AMC) method for the MIMO systems, where the receiver equipped with multiple antennas cooperatively recognizes the modulation types. Specifically, each received antenna gives their recognition sub-results via the CNN, respectively. Then, the decision maker identifies the modulation types with the recognition sub-results and cooperative decision rules, such as direct voting (DV), weighty voting (WV), direct averaging (DA) and weighty averaging (WA). The simulation results demonstrate that the Co-AMC method, based on the CNN and WA, has the highest correct classification probability in the four cooperative decision rules. In addition, the CNN-based Co-AMC method also performs better than the high order cumulants (HOC)-based traditional AMC methods, which shows the effective feature extraction and powerful classification capabilities of the CNN.

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