Abstract: The Internet of Things (IoT) is developed to ensure monitoring and connectivity within different applications. Thus, it is critical to study the channel propagation characteristics in Low Power Wide Area Network (LPWAN), especially LoRaWAN. In this paper, an in-depth investigation of the reciprocity between the uplink and downlink Channel State Information (CSI) is done by performing an outdoor measurement campaign in the area of Campus Beaulieu in Rennes. At each different location, the CSI reciprocity is quantified using the Pearson Correlation Coefficient (PCC) which shows a very high linear correlation between the uplink and downlink CSI. This reciprocity feature could be utilized for the physical layer security between the node and the gateway. On the other hand, most of the CSI shapes from different locations are highly uncorrelated with each other. Hence, it can be anticipated that this could achieve significant localization gain by utilizing the frequency hopping in the LoRa systems to get access to a wider band.
Abstract: In this paper, we consider the MU-MISO downlink scenario, under imperfect channel state information (CSI). The main issue in imperfect CSI is to keep the probability of each user achievable outage rate below the given threshold level. Such a rate outage constraints present significant and analytical challenges. There are many probabilistic methods are used to minimize the transmit optimization problem under imperfect CSI. Here, decomposition based large deviation inequality and Bernstein type inequality convex restriction methods are used to perform the optimization problem under imperfect CSI. These methods are used for achieving improved output quality and lower complexity. They provide a safe tractable approximation of the original rate outage constraints. Based on these method implementations, performance has been evaluated in the terms of feasible rate and average transmission power. The simulation results are shown that all the two methods offer significantly improved outage quality and lower computational complexity.
Abstract: Cesium iodide (CsI) melt was injected into anodic aluminum oxide (AAO) template and was solidified to CsI column. The controllable AAO channel size (10~500 nm) can makes CsI column size from 10 to 500 nm in diameter. In order to have a shorter light irradiate from each singe CsI column top to bottom the AAO template was coated a TiO2 nano-film. The TiO2 film acts a refraction film and makes X-ray has a shorter irradiation path in the CsI crystal making a stronger the photo-electron signal. When the incidence light irradiate from air (R=1.0) to CsI’s first surface (R=1.84) the first refraction happen, the first refraction continue into TiO2 film (R=2.88) and produces the low angle of the second refraction. Then the second refraction continue into AAO wall (R=1.78) and produces the third refraction after refractions between CsI and AAO wall (R=1.78) produce the fourth refraction. The incidence light through TiO2 filmand the first surface of CsI then arrive to the second surface of CsI. Therefore, the TiO2 film can has shorter refraction path of incidence light and increase the photo-electron conversion efficiency.
Abstract: This paper presents how the real-time chatter
prevention can be realized by feedback of acoustic cutting signal, and
the efficacy of the proposed adaptive spindle speed tuning algorithm is
verified by intensive experimental simulations. A pair of
microphones, perpendicular to each other, is used to acquire the
acoustic cutting signal resulting from milling chatter. A real-time
feedback control loop is constructed for spindle speed compensation
so that the milling process can be ensured to be within the stability
zone of stability lobe diagram. Acoustic Chatter Signal Index (ACSI)
and Spindle Speed Compensation Strategy (SSCS) are proposed to
quantify the acoustic signal and actively tune the spindle speed
respectively. By converting the acoustic feedback signal into ACSI,
an appropriate Spindle Speed Compensation Rate (SSCR) can be
determined by SSCS based on real-time chatter level or ACSI.
Accordingly, the compensation command, referred to as Added-On
Voltage (AOV), is applied to increase/decrease the spindle motor
speed. By inspection on the precision and quality of the workpiece
surface after milling, the efficacy of the real-time chatter prevention
strategy via acoustic signal feedback is further assured.
Abstract: This paper investigates the performance of Multiple- Input Multiple-Output (MIMO) feedback system combined with Orthogonal Frequency Division Multiplexing (OFDM). Two types of codebook based channel feedback techniques are used in this work. The first feedback technique uses a combination of both the long-term and short-term channel state information (CSI) at the transmitter, whereas the second technique uses only the short term CSI. The long-term and short-term CSI at the transmitter is used for efficient channel utilization. OFDM is a powerful technique employed in communication systems suffering from frequency selectivity. Combined with multiple antennas at the transmitter and receiver, OFDM proves to be robust against delay spread. Moreover, it leads to significant data rates with improved bit error performance over links having only a single antenna at both the transmitter and receiver. The effectiveness of these techniques has been demonstrated through the simulation of a MIMO-OFDM feedback system. The results have been evaluated for 4x4 MIMO channels. Simulation results indicate the benefits of the MIMO-OFDM channel feedback system over the one without incorporating OFDM. Performance gain of about 3 dB is observed for MIMO-OFDM feedback system as compared to the one without employing OFDM. Hence MIMO-OFDM becomes an attractive approach for future high speed wireless communication systems.
Abstract: We study the performance of compressed beamforming
weights feedback technique in generalized triangular decomposition
(GTD) based MIMO system. GTD is a beamforming technique that
enjoys QoS flexibility. The technique, however, will perform at its
optimum only when the full knowledge of channel state information
(CSI) is available at the transmitter. This would be impossible in
the real system, where there are channel estimation error and limited
feedback. We suggest a way to implement the quantized beamforming
weights feedback, which can significantly reduce the feedback data,
on GTD-based MIMO system and investigate the performance of
the system. Interestingly, we found that compressed beamforming
weights feedback does not degrade the BER performance of the
system at low input power, while the channel estimation error
and quantization do. For comparison, GTD is more sensitive to
compression and quantization, while SVD is more sensitive to the
channel estimation error. We also explore the performance of GTDbased
MU-MIMO system, and find that the BER performance starts
to degrade largely at around -20 dB channel estimation error.
Abstract: All the available algorithms for blind estimation namely constant modulus algorithm (CMA), Decision-Directed Algorithm (DDA/DFE) suffer from the problem of convergence to local minima. Also, if the channel drifts considerably, any DDA looses track of the channel. So, their usage is limited in varying channel conditions. The primary limitation in such cases is the requirement of certain overhead bits in the transmit framework which leads to wasteful use of the bandwidth. Also such arrangements fail to use channel state information (CSI) which is an important aid in improving the quality of reception. In this work, the main objective is to reduce the overhead imposed by the pilot symbols, which in effect reduces the system throughput. Also we formulate an arrangement based on certain dynamic Artificial Neural Network (ANN) topologies which not only contributes towards the lowering of the overhead but also facilitates the use of the CSI. A 2×2 Multiple Input Multiple Output (MIMO) system is simulated and the performance variation with different channel estimation schemes are evaluated. A new semi blind approach based on dynamic ANN is proposed for channel tracking in varying channel conditions and the performance is compared with perfectly known CSI and least square (LS) based estimation.