scholarly journals Cross-Layer Inference in WSN: From Methods to Experimental Validation

2020 ◽  
Author(s):  
Indrakshi Dey
Keyword(s):  
Mimo System ◽  
Hypothesis Test ◽  
Multiple Antennas ◽  
Statistical Signal Processing ◽  
Decision Fusion ◽  
Inference Problem ◽  
Robust Detection ◽  
Gas Leakage ◽  
Input Multiple Output ◽  
Set Up

In this chapter, the fundamentals of distributed inference problem in wireless sensor networks (WSN) is addressed and the statistical theoretical foundations to several applications is provided. The chapter adopts a statistical signal processing perspective and focusses on distributed version of the binary-hypothesis test for detecting an event as correctly as possible. The fusion center is assumed to be equipped with multiple antennas collecting and processing the information. The inference problem that is solved, primarily concerns the robust detection of a phenomenon of interest (for example, environmental hazard, oil/gas leakage, forest fire). The presence of multiple antennas at both transmit and receive sides resembles a multiple-input-multiple-output (MIMO) system and allows for utilization of array processing techniques providing spectral efficiency, fading mitigation and low energy sensor adoption. The problem is referred to as MIMO decision fusion. Subsequently, both design and evaluation (simulated and experimental) of these fusion approaches is presented for this futuristic WSN set-up.

scholarly journals Deep Learning Based Antenna Selection for MIMO SDR System

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6987
Author(s):  
Shida Zhong ◽  
Haogang Feng ◽  
Peichang Zhang ◽  
Jiajun Xu ◽  
Huancong Luo ◽  
...  
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Software Defined Radio ◽  
Antenna Selection ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Input Multiple Output ◽  
Set Up ◽  
Time Division

In this paper, we propose and implement a novel framework of deep learning based antenna selection (DLBAS)-aided multiple-input–multiple-output (MIMO) software defined radio (SDR) system. The system is constructed with the following three steps: (1) a MIMO SDR communication platform is first constructed, which is capable of achieving uplink communication from users to the base station via time division duplex (TDD); (2) we use the deep neural network (DNN) from our previous work to construct a deep learning decision server to assist the MIMO SDR platform for making intelligent decision for antenna selection, which transforms the optimization-driven decision making method into a data-driven decision making method; and (3) we set up the deep learning decision server as a multithreading server to improve the resource utilization ratio. To evaluate the performance of the DLBAS-aided MIMO SDR system, a norm-based antenna selection (NBAS) scheme is selected for comparison. The results show that the proposed DLBAS scheme performed equally to the NBAS scheme in real-time and out-performed the MIMO system without AS with up to 53% improvement on average channel capacity gain.

scholarly journals Implementation and analysis of MIMO system based on Alamouti coding

2019 ◽  
Author(s):  
Vidhya Lavanya Ramachandran
Keyword(s):  
Wireless Communication ◽  
Spatial Data ◽  
Communication System ◽  
Channel Model ◽  
Mimo System ◽  
Multiple Antennas ◽  
Space Time ◽  
Alamouti Coding ◽  
Multiple Input ◽  
Input Multiple Output

Use of multiple antennas at the receiver and transmitter in a wireless network is a rapidly emerging technology that promises higher data rates at longer ranges without consuming extra bandwidth or transmit power. These systems can be with single-input multiple-output (SIMO), multiple-input single-output (MISO), or with multiple-input multiple-output (MIMO) architectures to improve the signal quality at the receiver using multiple data pipes over a link. MIMO communication system considers multiple antennas used at the transmitting end as well as the receiving end. In addition, the MIMO system has the ability to spread in the spatial domain which is combined in such a way that they either create effective multiple parallel spatial data pipes and diversity to improve the quality (for example decrease the bit error rate). The benefit from the multiple antennas arise from the new dimension space. Hence, the spatial dimension comes as a complement to time. MIMO technology also known as space-time ‘wirelesses’. Space–time block coding (STBC) is a technique used in wireless communications to transmit multiple copies of a data stream across a number of antennas and to exploit the various received versions of the data to improve the reliability of data transfer. The Alamouti coding is a STBC coding technique that is widely use in wireless communication. Alamouti coding can be used in different models like using 2×1 MISO mode or a 2×2 MIMO mode and it can use OFDM system. The purpose of this project is to test a performance of wireless communication system under different type of noise, and the channel model. Also, implementation and analysis of MIMO system based on Alamouti STBC coding.

scholarly journals Implementation of a Zero-Forcing Precoding Algorithm Combined with Adaptive Beamforming Based on WiMAX System

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Hyunwook Yang ◽  
Seungwon Choi
Keyword(s):  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Experimental System ◽  
Multiple Access Interference ◽  
Adaptive Beamforming ◽  
Base Station ◽  
Zero Forcing ◽  
Single Antenna ◽  
Input Multiple Output ◽  
Wimax System

We propose a novel precoding algorithm that is a zero-forcing (ZF) method combined with adaptive beamforming in the Worldwide Interoperability for Microwave Access (WiMAX) system. In a Multiuser Multiple-Input Multiple-Output (MU-MIMO) system, ZF is used to eliminate the Multiple Access Interference (MAI) in order to allow several users to share a common resource. The adaptive beamforming algorithm is used to achieve the desired SNR gain. The experimental system consists of a WiMAX base station that has 2 MIMO elements, each of which is composed of three-array antennas and two mobile terminals, each of which has a single antenna. Through computer simulations, we verified that the proposed method outperforms the conventional ZF method by at least 2.4 dB when the BER is 0.1%, or 1.7 dB when the FER is 1%, in terms of the SNR. Through a hardware implementation of the proposed method, we verified the feasibility of the proposed method for realizing a practical WiMAX base station to utilize the channel resources as efficiently as possible.

A companding approach for PAPR suppression in OFDM based massive MIMO system

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ajay Kumar Yadav ◽  
Pritam Keshari Sahoo ◽  
Yogendra Kumar Prajapati
Keyword(s):  
Massive Mimo ◽  
Orthogonal Frequency Division Multiplexing ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Average Power ◽  
Base Station ◽  
Convex Optimization Problem ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Mimo Ofdm

Abstract Orthogonal frequency division multiplexing (OFDM) based massive multiuser (MU) multiple input multiple output (MIMO) system is popularly known as high peak-to-average power ratio (PAPR) issue. The OFDM-based massive MIMO system exhibits large number of antennas at Base Station (BS) due to the use of large number of high-power amplifiers (HPA). High PAPR causes HPAs to work in a nonlinear region, and hardware cost of nonlinear HPAs are very high and also power inefficient. Hence, to tackle this problem, this manuscript suggests a novel scheme based on the joint MU precoding and PAPR minimization (PP) expressed as a convex optimization problem solved by steepest gradient descent (GD) with μ-law companding approach. Therefore, we develop a new scheme mentioned to as MU-PP-GDs with μ-law companding to minimize PAPR by compressing and enlarging of massive MIMO OFDM signals simultaneously. At CCDF = 10−3, the proposed scheme (MU-PP-GDs with μ-law companding for Iterations = 100) minimizes the PAPR to 3.70 dB which is better than that of MU-PP-GDs, (iteration = 100) as shown in simulation results.

An Improved MMSE Pre-Coding Method for the Downlink Multi-User MIMO System

2012 ◽  
Vol 457-458 ◽  
pp. 600-606
Author(s):  
Xian Kun Gao ◽  
Yan Cui ◽  
Ji Lai Ying ◽  
Yong Chang Yu
Keyword(s):  
Numerical Simulations ◽  
Mimo System ◽  
Multiple Antennas ◽  
Poor Performance ◽  
Inversion Method ◽  
Block Diagonalization ◽  
Coding Method ◽  
Diagonalization Method ◽  
Noise Ratio ◽  
Theoretical Analyses

Recently many practical downlink multi-user MIMO linear pre-coding methods have been proposed, such as the channel inversion method and the block diagonalization method (BD). Considering the channel inversion method based on MMSE criterion (MMSE-CI) which is confined to a single receives antenna case, the BD has more advantages in multiple antennas cases, however, it has poor performance at the low and medium SNR regime on account of no consideration on the noise. In this paper, an improved MMSE pre-coding method is proposed with multi receive antennas of each user. Based on MMSE-CI, the cooperation of multiple antennas is adopted to further suppress the residual interference during designing the pre-coding matrix, which could increase the signal-to- interference-plus-noise ratio (SINR) at each user’s receiver. The proposed method obtains a better performance than the MMSE-CI and the BD algorithms, and its effectiveness is validated by both theoretical analyses and numerical simulations.

A compact dual port UWB-MIMO/diversity antenna for indoor application

2017 ◽  
Vol 10 (3) ◽  
pp. 360-367 ◽  
Author(s):  
Sonika Priyadarsini Biswal ◽  
Sushrut Das
Keyword(s):  
Mimo System ◽  
Ground Plane ◽  
Multiple Input Multiple Output ◽  
Open Circuit ◽  
Ultra Wideband ◽  
Impedance Bandwidth ◽  
Good Prospect ◽  
Mimo Antenna ◽  
Radiating Element ◽  
Input Multiple Output

A compact printed quadrant shaped monopole antenna is introduced in this paper as a good prospect for ultra wideband- multiple-input multiple-output (UWB-MIMO) system. The proposed MIMO antenna comprises two perpendicularly oriented monopoles to employ polarization diversity. An open circuit folded stub is extended from the ground plane of each radiating element to enhance the impedance bandwidth satisfying the UWB criteria. Two ‘L’ shaped slots are further etched on the radiator to provide good isolation performance between two radiators. The desirable radiator performances and diversity performances are ensured by simulation and/or measurement of the reflection coefficient, radiation pattern, realized peak gain, envelope correlation coefficient (ECC), diversity gain, mean effective gain (MEG) ratio and channel capacity loss (CCL). Results indicate that the proposed antenna exhibits 2.9–11 GHz 10 dB return loss bandwidth, mutual coupling <−20 dB, ECC < 0.003, MEG ratio ≈ 1, and CCL < 0.038 Bpsec/Hz, making it a good candidate for UWB and MIMO diversity application.

scholarly journals Energy and Information Beamforming in Airborne Massive MIMO System for Wireless Powered Communications

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3540 ◽  
Author(s):  
Yurong Wang ◽  
Aijun Liu ◽  
Kui Xu ◽  
Xiaochen Xia
Keyword(s):  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Hardware Complexity ◽  
Mimo Channels ◽  
Channel State ◽  
Active Antennas ◽  
Large Numbers ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Critical Problems

Energy supply and information backhaul are critical problems for wireless sensor networks deployed in remote places with poor infrastructure. To deal with these problems, this paper proposes an airborne massive multiple-input multiple-output (MIMO) system for wireless energy transfer (WET) and information transmission. An air platform (AP) equipped with a two-dimensional rectangular antenna array is employed to broadcast energy and provide wireless access for ground sensors. By exploiting the statistical property of air-terrestrial MIMO channels, the energy and information beamformers are jointly designed to maximize the average received signal-to-interference-plus-noise ratio (SINR), which gives rise to a statistical max-SINR beamforming scheme. The scheme does not rely on the instantaneous channel state information, but still requires large numbers of RF chains at AP. To deal with this problem, a heuristic strongest-path energy and information beamforming scheme is proposed, which can be implemented in the analog-domain with low computational and hardware complexity. The analysis of the relation between the two schemes reveals that, with proper sensor scheduling, the strongest-path beamforming is equivalent to the statistical max-SINR beamforming when the number of AP antennas tends to infinity. Using the asymptotic approximation of average received SINR at AP, the system parameters, including transmit power, number of active antennas of AP and duration of WET phase, are optimized jointly to maximize the system energy efficiency. The simulation results demonstrate that the proposed schemes achieve a good tradeoff between system performance and complexity.

Sign in / Sign up

Export Citation Format

Share Document