Detection of Incoherent Signal In Molecular Communication Based on Channel Impulse Response

In order to solve the problem of intercode interference (ISI) and background noise caused by molecular diffusion in molecular communication, Honda analyzed and studied four methods to resist ISI signal, and analyzed the characteristics of the received signal at the moment. A reliable incoherent molecular signal detection algorithm independent of channel impulse response (CIR) is proposed, and an adaptive threshold calculation method is designed, and the theoretical value of bit error rate (BER) is given. The simulation results show that the proposed scheme BER is lower than the traditional scheme BER under the same computational complexity, so it has a wide application prospect in the nanoscale molecular communication system with limited computing power.

Estimation of Compressible Channel Impulse Response for OFDM Modulated Transmissions

Channel estimation scheme for OFDM modulated transmissions usually combines an initial block-pilot-assisted stage with a tracking one based on comb or scattered pilots distributed among user data in the signal frame. The channel reconstruction accuracy in the former stage has a significant impact on tracking efficiency of the channel variations and the overall transmission quality. The paper presents a new block-pilot-assisted channel reconstruction procedure based on the DFT-based approach and the Least Square impulse response estimation. The proposed method takes into account a compressibility feature of the channel impulse response and restores its coefficients in groups of automatically controlled size. The proposition is analytically explained and tested in a OFDM simulation environment. The popular DFT-based methods including compressed sensing oriented one were used as references for comparison purposes. The obtained results show a quality improvement in terms of Bit Error Rate and Mean Square Error measures in low and mid ranges of signal-to-noise ratio without significant computational complexity growth in comparison to the classical DFT-based solutions. Moreover, additional multiplication operations can be eliminated, compared to the competitive, in terms of estimation quality, compressed sensing reconstruction method based on greedy approach.

Adaptive Regularized Zero-Forcing Beam Forming In Massive MIMO With Multi-Antenna Users

We are testing the proposed approach in several scenarios generated using Quadriga - an open source software for generating realistic radio channel impulse response. <br>

Adaptive Regularized Zero-Forcing Beam Forming In Massive MIMO With Multi-Antenna Users

We are testing the proposed approach in several scenarios generated using Quadriga - an open source software for generating realistic radio channel impulse response. <br>

CIR-Based Device-Free People Counting via UWB Signals

The outbreak of COVID-19 has resulted in many different policies being adopted across the world to reduce the spread of the virus. These policies include wearing surgical masks, hand hygiene practices, increased social distancing and full country-wide lockdown. Specifically, social distancing involves keeping a certain distance from others and avoiding gathering together in large groups. Automatic crowd density estimation is a technological solution that could help in guaranteeing social distancing by reducing the probability that two persons in a public area come in close proximity to each other while moving around. This paper proposes a novel low complexity RF sensing system for automatic people counting based on low cost UWB transceivers. The proposed system is based on an ordinary classifier that exploits features extracted from the channel impulse response of UWB communication signals. Specifically, features are extracted from the sorted list of singular values obtained from the singular value decomposition applied to the matrix of the channel impulse response vector differences. Experimental results achieved in two different environments show that the proposed system is a promising candidate for future automatic crowd density monitoring systems.