Assessment of Cognitive Communications Interest Areas for NASA Needs and Benefits

This effort provides a survey and assessment of various cognitive communications interest areas, including node-to-node link optimization, intelligent routing/networking, and learning algorithms, and is conducted primarily from the perspective of NASA space communications needs and benefits. Areas of consideration include optimization methods, learning algorithms, and candidate implementations/technologies. Assessments of current research efforts are provided with mention of areas for further investment. Other considerations, such as antenna technologies and cognitive radio platforms, are briefly provided as well

On the performance of the time reversal SM-MIMO-UWB system on correlated channels

The impact of spatial correlation on the multi-input multi-output ultrawide band (MIMO-UWB) system using the time reversal (TR) technique is investigated. Thanks to TR, several data streams can be transmitted by using only one antenna in a system named virtual MIMO-TRUWB. Since the virtual MIMO-TR-UWB system is not affected by the transmit correlation, under the condition of the high spatial correlation, it outperforms the true MIMO-UWB system with multiple transmit antennas. The channel measurements are performed in short-range indoor environment, both line-of-sight and non-line-of-sight to verify the adopted correlated channel model.Vietnamese National Foundation for Science and Technology Development (NAFOSTED)/102.02.07.0

Ultra-Wideband Secure Communications and Direct RF Sampling Transceivers

Larger wireless device bandwidth results in new capabilities in terms of higher data rates and security. The 5G evolution is focus on exploiting larger bandwidths for higher though-puts. Interference and co-existence issues can also be addressed by the larger bandwidth in the 5G and 6G evolution. This dissertation introduces of a novel Ultra-wideband (UWB) Code Division Multiple Access (CDMA) technique to exploit the largest bandwidth available in the upcoming wireless connectivity scenarios. The dissertation addresses interference immunity, secure communication at the physical layer and longer distance communication due to increased receiver sensitivity. The dissertation presents the design, workflow, simulations, hardware prototypes and experimental measurements to demonstrate the benefits of wideband Code-Division-Multiple-Access. Specifically, a description of each of the hardware and software stages is presented along with simulations of different scenarios using a test-bench and open-field measurements. The measurements provided experimental validation carried out to demonstrate the interference mitigation capabilities. In addition, Direct RF sampling techniques are employed to handle the larger bandwidth and avoid analog components. Additionally, a transmit and receive chain is designed and implemented at 28 GHz to provide a proof-of-concept for future 5G applications. The proposed wideband transceiver is also used to demonstrate higher accuracy direction finding, as much as 10 times improvement

Next generation >200 Gb/s multicore fiber short-reach networks employing machine learning

This work proposes and evaluates the use of machine learning (ML) techniques on >200 Gb/s short-reach systems employing weakly coupled multicore fiber (MCF) and Kramers-Kronig (KK) receivers. The short-reach systems commonly found in intra data centers (DC) connections demand low cost-efficient direct detection receivers (DD). The KK receivers allow the combination of higher modulation order, such as 16-QAM used in coherent systems, with the low complexity and low cost of DD. Thus, the use of KK receivers allows to increase the bit rate and spectral efficiency while maintaining the cost of DD systems as this is an important requirement in DC. The use of MCF allows to increase the system capacity as well as the system cable density, although the use of MCF induces additional distortion, known as inter-core crosstalk (ICXT), to the system. Thus, low complexity ML techniques such as k-means clustering, k nearest neighbor (KNN) and artificial neural network (ANN) (estimation feedforward neural network (FNN) and classification feedforward neural network) are proposed to mitigate the effects of random ICXT. The performance improvement provided by the k-means clustering, KNN and the two types of FNN techniques is assessed and compared with the system performance obtained without the use of ML. The use of estimation and classification FNN prove to significantly improve the system performance by mitigating the impact of the ICXT on the received signal. This is achieved by employing only 10 neurons in the hidden layer and four input features. It has been shown that k-means or KNN techniques do not provide performance improvement compared to the system without using ML. These conclusions are valid for direct detection MCF-based short-reach systems with the product between the skew (relative time delay between cores) and the symbol rate much lower than one (skew x symbol rate « 1). By employing the proposed ANNs, the system shows an improvement of approximately 12 dB on the ICXT level, for the same outage probability when comparing with the system without the use of ML. For the BER threshold of 10−1.8 and compared with the standard system operating without employing ML techniques, the system operating with the proposed ANNs show a received optical power improvement of almost 3 dB and a ICXT level improvement of approximately 9 dB when the mean BER is analized.Este trabalho propõe e avalia o uso de técnicas de machine learning (ML) em sistemas de curto alcance com ritmo binário superior a 200 Gb/s utilizando receptores Kramers-Kronig (KK) e fibras multinúcleo (MCF). Os sistemas de curto alcance usualmente encontrados em conexões intra-data centers (DC) exigem receptores de deteção direta (DD) de baixo custo. Os receptores KK permitem a combinação de sistemas de modulação de maior ordem, tais como o 16-QAM, usado em sistemas coerentes, com o baixo custo dos receptores DD. Portanto, o uso de receptores KK permite melhorar o ritmo binário e eficiência espectral e manter a eficiência de custo dos sistemas DD, o que é importante em DC. O uso de fibras multinúcleo permite o aumento da capacidade do sistema, bem como a densidade de cabos. No entanto, o uso de MCF introduz uma distorção adicional no sistema conhecida como inter-core crosstalk (ICXT). Para mitigar os efeitos do ICXT aleatório, são propostas e avaliadas técnicas de ML de baixa complexidade como k-means clustering, k nearest neighbor (KNN) e rede neuronais artificiais (ANN). O desempenho associado à utilização de algoritmos de ML (k-means, KNN e duas redes neuronais do tipo feedforward (FNN): uma para estimação e outra para classificação), é avaliado e comparado com o desempenho do sistema obtido sem o uso de ML. A utilização de FNN para estimação e classificação conduziram a uma melhoria significativa no desempenho do sistema, mitigando o impacto do ICXT no sinal recebido. Isso é alcançado com o uso de uma rede neuronal com uma arquitetura muito simples contendo quatro entradas e 10 neurónios na camada escondida. Foi demonstrado que os algoritmos k-means e KNN não proporcionam melhoria de desempenho em comparação com o sistema sem o uso de ML. Essas conclusões são válidas para sistemas DD de curto alcance baseados em MCF com o produto entre o skew (atraso relativo entre os núcleos) e o ritmo de símbolos muito menor que um (skew x symbol rate « 1). Com o uso das ANNs, o sistema apresenta uma melhoria de aproximadamente 12 dB na probabilidade de indisponibilidade quando comparado com o sistema sem o uso de ML. Para o limite de BER de 10−1.8 , e comparado com o sistema padrão sem o uso de técnicas de ML, o sistema com o uso de ANN mostra uma melhoria na potência óptica recebida de quase 3 dB e uma melhoria no nível de ICXT de aproximadamente 9 dB em relação ao BER médio

Machine Learning for Multi-Layer Open and Disaggregated Optical Networks

L'abstract è presente nell'allegato / the abstract is in the attachmen