Visible Light Wireless Data Center Links with Distinct Beam Configurations

Visible light communication (VLC) is being explored as one promising approach to enable wireless data centers (WDC). Up to now, the visible light wireless data center links are still limited to the conventional Lambertian beam paradigm. The potential coverage gain relevant to the optical beam space is waiting for sufficient investigation. For addressing this issue, in this paper, the dynamic optical beam based WDC coverage enhancement scheme is introduced, and for each transmitter, the best candidate asymmetrical optical beam is selected to load the data signal. Numerical evaluation shows that, compared with the conventional static beam configuration, up to 6.76 dB peak signal to noise ratio (SNR) gain and 4.46 dB average SNR gain could be provided by the proposed dynamic beam scheme. Moreover, this SNR dynamic range is reduced to 36.65 dB while the counterpart of the static non-Lambertian beam configuration is up to 44.78 dB.

Near-channel classifier: symbiotic communication and classification in high-dimensional space

Brain-inspired high-dimensional (HD) computing represents and manipulates data using very long, random vectors with dimensionality in the thousands. This representation provides great robustness for various classification tasks where classifiers operate at low signal-to-noise ratio (SNR) conditions. Similarly, hyperdimensional modulation (HDM) leverages the robustness of complex-valued HD representations to reliably transmit information over a wireless channel, achieving a similar SNR gain compared to state-of-the-art codes. Here, we first propose methods to improve HDM in two ways: (1) reducing the complexity of encoding and decoding operations by generating, manipulating, and transmitting bipolar or integer vectors instead of complex vectors; (2) increasing the SNR gain by 0.2 dB using a new soft-feedback decoder; it can also increase the additive superposition capacity of HD vectors up to 1.7$$\times$$ × in noise-free cases. Secondly, we propose to combine encoding/decoding aspects of communication with classification into a single framework by relying on multifaceted HD representations. This leads to a near-channel classification (NCC) approach that avoids transformations between different representations and the overhead of multiple layers of encoding/decoding, hence reducing latency and complexity of a wireless smart distributed system while providing robustness against noise and interference from other nodes. We provide a use-case for wearable hand gesture recognition with 5 classes from 64 EMG sensors, where the encoded vectors are transmitted to a remote node for either performing NCC, or reconstruction of the encoded data. In NCC mode, the original classification accuracy of 94% is maintained, even in the channel at SNR of 0 dB, by transmitting 10,000-bit vectors. We remove the redundancy by reducing the vector dimensionality to 2048-bit that still exhibits a graceful degradation: less than 6% accuracy loss is occurred in the channel at − 5 dB, and with the interference from 6 nodes that simultaneously transmit their encoded vectors. In the reconstruction mode, it improves the mean-squared error by up to 20 dB, compared to standard decoding, when transmitting 2048-dimensional vectors.

Solution for the Problems in RADAR Systems from SNR Gain Expressions and Blackout Likelihood Detection for Aviation Systems

Issues confronted in Radar frameworks at SDSC are, It is exceptionally troublesome for them to track an flying machine and discover its point, run and rise. With the existing ACSs, being incapable to provide the required information capacity. Since this misfortune of communication takes put between the ground station to the discuss station, discuss crashes may happen. Thus to dodge these kind of issues, modern proposed framework is defined. The full Duplex and Half Duplex systems can be combined to form a Hybrid Duplex Systems (HBD-ACS). In the existing Half duplex system, there was a crux in the spectrum mainly in the aeronautical industry. Hence the new HBD-ACS in proposed which eliminates the progressive interference. Progressive interference detector includes two methods namely, expressions of SNR gain and blackout probability. The Full duplex system may consists of the leftover interference. Hence to eliminate this, Interference Ignorant detector and SIC detector are used. Hence from the HBD-ACS, the diversity gain will be non-zero and the interference can be limited.

SIGNAL-TO-NOISE RATIO GAIN VIA CORRELATED NOISE IN AN ENSEMBLE OF NOISY NEURONS

The collective response of an ensemble of leaky integrate-and-fire neurons induced by local correlated noise is investigated theoretically. Based on the linear response theory, we derive the analytic expression of signal-to-noise ratio (SNR). Numerical results show that the amplitude of internal noise can be increased up to an optimal value where the output SNR reaches a maximum value. Interestingly, we find that the correlated noise between the nearest neurons could lead to the obvious SNR gain. We also show that the SNR can reach unity under condition that the correlated noise between the nearest neurons is negative. This nonlinear amplification of SNR gain in an ensemble of noisy neurons can be related to the array stochastic resonance (SR) phenomenon. Furthermore, we also show that the SNR gain can also be optimized by tuning the number of neuron units, frequency and amplitude of the weak periodic signal.