Relay-aided Interference Alignment in Wireless Networks

Resource management in wireless networks is one of the key factors in maximizing the overall throughput. Contrary to popular belief, dividing the resources in a dense network does not yield the best results. A method that has been developed recently shares the spectrum amongst all the users in such a way that each node can potentially utilize about half of all the available resources. This new technique is often referred to as Interference Alignment and excels based on the fact that the amount of the network resources assigned to a user does not go to zero as the number of users in the network increases. Unfortunately it is still very difficult to implement the interference alignment concepts in practice. This thesis investigates some of the low-complexity solutions to integrate interference alignment ideas into the existing wireless networks. In the third and fourth chapters of this thesis, it is shown that introducing relays to a quasi-static wireless network can be very beneficial in terms of achieving higher degrees of freedom. The relays store the signals being communicated in the network and then send a linear combination of those signals. Using the proposed scheme, it is shown that although the relays cannot decode the original information, they can transform the equivalent channel in such a way that performing interference alignment becomes much easier. Investigating the required output power of the relays shows that it can scale either slower or faster than the output power of the main transmitters. This opens new doors for the applications that have constraints on the accessible output powers in the network nodes. The results are valid for both $X$ Channel and Interference Channel network topologies. In Chapter Five, the similarities between full-duplex transmitters and relays are examined. The results suggest that the transmitters can play the relay roles for offering easier interference alignment. Similar to the relay-based alignment, in the presented scheme full-duplex transmitters listen to the signals from other transmitters and use this information during the subsequent transmission periods. Studying the functionality of the full-duplex transmitters from the receivers' side shows the benefits of having a minimal cooperation between transmitters without even being able to decode the signals. It is also proved that the degrees of freedom for the $N$-user Interference Channel with full-duplex transmitters can be $\sqrt{\frac{N}{2}}$. The results offer an easy way to recover a portion of degrees of freedom with manageable complexity suited for practical systems

Linear Combinatorial Semi-Bandit with Causally Related Rewards

In a sequential decision-making problem, having a structural dependency amongst the reward distributions associated with the arms makes it challenging to identify a subset of alternatives that guarantees the optimal collective outcome. Thus, besides individual actions' reward, learning the causal relations is essential to improve the decision-making strategy. To solve the two-fold learning problem described above, we develop the 'combinatorial semi-bandit framework with causally related rewards', where we model the causal relations by a directed graph in a stationary structural equation model. The nodal observation in the graph signal comprises the corresponding base arm's instantaneous reward and an additional term resulting from the causal influences of other base arms' rewards. The objective is to maximize the long-term average payoff, which is a linear function of the base arms' rewards and depends strongly on the network topology. To achieve this objective, we propose a policy that determines the causal relations by learning the network's topology and simultaneously exploits this knowledge to optimize the decision-making process. We establish a sublinear regret bound for the proposed algorithm. Numerical experiments using synthetic and real-world datasets demonstrate the superior performance of our proposed method compared to several benchmarks

Evaluation of mammalian codon usage of fimH in DNA vaccine design

Uropathogenic Escherichia coli (UPEC) bacteria are the principal cause of urinary tract infections (UTI). Because these bacteria propagate intracellularly, the cellular immune response is an important factor in UTIs. Therefore, we designed a genetic construct to induce a cellular immune response. In order to develop a genetic construct that induces strong cellular immunity against this pathogen, we used the fimH synthetic gene according to mammalian codon usage, and the gene expression was compared with wild type codon usage. Initially, we designed two constructs, pVAX/fimH mam and pVAX/fimH wt, which contain mammalian and wild type codon usage, respectively. The Cos-7 cell line was transfected separately with a complex of pVAX/fimH mam-ExGene 500 poly cationic polymer and pVAX/fimH wt-ExGene 500 poly cationic polymer. Expression of the fimH gene in both constructs in COS7 cells was confirmed by RT-PCR, SDS-PAGE, and Western blotting. Both of the pVAX/fimH cassettes expressed inserted fimH genes (mam and wt) in Cos-7 cells. Our results suggest that codon optimization successfully expressed the fimH gene because the fimH gene with mammalian codon usage is compatible with the eukaryotic expression system. Therefore, mammalian codon usage could be appropriate in a pVAX/fimH construct as a DNA vaccine

Spatiotemporal Variations of Air Pollution during the COVID-19 Pandemic across Tehran, Iran: Commonalities with and Differences from Global Trends

The COVID-19 pandemic has induced changes in global air quality, mostly short-term improvements, through worldwide lockdowns and restrictions on human mobility and industrial enterprises. In this study, we explored the air pollution status in Tehran metropolitan, the capital city of Iran, during the COVID-19 outbreak. To this end, ambient air quality data (CO, NO2, O3, PM10, SO2, and AQI) from 14 monitoring stations across the city, together with global COVID-19-related records, were utilized. The results showed that only the annual mean concentration of SO2 increased during the COVID-19 pandemic, mainly due to burning fuel oil in power plants. The findings also demonstrated that the number of days with a good AQI has significantly decreased during the pandemic, despite the positive trend in the global AQI. Based on the spatial variation of the air quality data across the city, the results revealed that increasing pollution levels were more pronounced in low-income regions