Mesh network model for urban area

Thesis (S.M.)--Massachusetts Institute of Technology, System Design and Management Program, 2008.Includes bibliographical references (p. 52, 2-7 (2nd group)).Decreasing population, high crime rate, and limited economic opportunities are all symptoms of urban decline. These characteristics are, unfortunately, evident in major cities and small towns. Local municipalities in these cities and towns with the aid of state and federal government have attempted to reverse urban decline through the traditional approach of urban renewal. Their idea was to create low cost housing to attract people back to urban areas. Their approach has shown mixed results with most attempts having no effect on the deterioration. The goal of this thesis is to propose a higher system approach to answer urban decline through the application of new technology, wireless mesh networks. A wireless mesh network can provide improved security, public safety, new economic opportunities, and a bridge that crosses the digital divide. Married to the appropriate applications, a wireless mesh network creates a business model that is both favorable and sustainable. More importantly, the business model brings about the human capital necessary for urban revitalization.by Nhan Tu Chiang.S.M

Conditional expectation with regularization for missing data imputation

Missing data frequently occurs in datasets across various domains, such as medicine, sports, and finance. In many cases, to enable proper and reliable analyses of such data, the missing values are often imputed, and it is necessary that the method used has a low root mean square error (RMSE) between the imputed and the true values. In addition, for some critical applications, it is also often a requirement that the imputation method is scalable and the logic behind the imputation is explainable, which is especially difficult for complex methods that are, for example, based on deep learning. Based on these considerations, we propose a new algorithm named "conditional Distribution-based Imputation of Missing Values with Regularization" (DIMV). DIMV operates by determining the conditional distribution of a feature that has missing entries, using the information from the fully observed features as a basis. As will be illustrated via experiments in the paper, DIMV (i) gives a low RMSE for the imputed values compared to state-of-the-art methods; (ii) fast and scalable; (iii) is explainable as coefficients in a regression model, allowing reliable and trustable analysis, makes it a suitable choice for critical domains where understanding is important such as in medical fields, finance, etc; (iv) can provide an approximated confidence region for the missing values in a given sample; (v) suitable for both small and large scale data; (vi) in many scenarios, does not require a huge number of parameters as deep learning approaches; (vii) handle multicollinearity in imputation effectively; and (viii) is robust to the normally distributed assumption that its theoretical grounds rely on

Secondary Network Throughput Optimization of NOMA Cognitive Radio Networks Under Power and Secure Constraints

Recently, the combination of cognitive radio networks with the nonorthogonal multiple access (NOMA) approach has emerged as a viable option for not only improving spectrum usage but also supporting large numbers of wireless communication connections. However, cognitive NOMA networks are unstable and vulnerable because multiple devices operate on the same frequency band. To overcome this drawback, many techniques have been proposed, such as optimal power allocation and interference cancellation. In this paper, we consider an approach by which the secondary transmitter (STx) is able to find the best licensed channel to send its confidential message to the secondary receivers (SRxs) by using the NOMA technique. To combat eavesdroppers and achieve reasonable performance, a power allocation policy that satisfies both the outage probability (OP) constraint of primary users and the security constraint of secondary users is optimized. The closed-form formulas for the OP at the primary base station and the leakage probability for the eavesdropper are obtained with imperfect channel state information. Furthermore, the throughput of the secondary network is analyzed to evaluate the system performance. Based on that, two algorithms (i.e., the continuous genetic algorithm (CGA) for CR NOMA (CGA-CRN) and particle swarm optimization (PSO) for CR NOMA (PSO-CRN)), are applied to optimize the throughput of the secondary network. These optimization algorithms guarantee not only the performance of the primary users but also the security constraints of the secondary users. Finally, simulations are presented to validate our research results and provide insights into how various factors affect system performance

Secondary Network Throughput Optimization of NOMA Cognitive Radio Networks Under Power and Secure Constraints

Recently, the combination of cognitive radio networks with the nonorthogonal multiple access (NOMA) approach has emerged as a viable option for not only improving spectrum usage but also supporting large numbers of wireless communication connections. However, cognitive NOMA networks are unstable and vulnerable because multiple devices operate on the same frequency band. To overcome this drawback, many techniques have been proposed, such as optimal power allocation and interference cancellation. In this paper, we consider an approach by which the secondary transmitter (STx) is able to find the best licensed channel to send its confidential message to the secondary receivers (SRxs) by using the NOMA technique. To combat eavesdroppers and achieve reasonable performance, a power allocation policy that satisfies both the outage probability (OP) constraint of primary users and the security constraint of secondary users is optimized. The closed-form formulas for the OP at the primary base station and the leakage probability for the eavesdropper are obtained with imperfect channel state information. Furthermore, the throughput of the secondary network is analyzed to evaluate the system performance. Based on that, two algorithms (i.e., the continuous genetic algorithm (CGA) for CR NOMA (CGA-CRN) and particle swarm optimization (PSO) for CR NOMA (PSO-CRN)), are applied to optimize the throughput of the secondary network. These optimization algorithms guarantee not only the performance of the primary users but also the security constraints of the secondary users. Finally, simulations are presented to validate our research results and provide insights into how various factors affect system performance

Throughput Optimization for NOMA Energy Harvesting Cognitive Radio with Multi-UAV-Assisted Relaying under Security Constraints

This paper investigates the throughput of a non-orthogonal multiple access (NOMA)-based cognitive radio (CR) system with multiple unmanned aerial vehicle (UAV)-assisted relays under system performance and security constraints. We propose a communication protocol that includes an energy harvesting (EH) phase and multiple communication phases. In the EH phase, the multiple UAV relays (URs) harvest energy from a power beacon. In the first communication phase, a secondary transmitter (ST) uses the collected energy to send confidential signals to the first UR using NOMA. Simultaneously, a ground base station communicates with a primary receiver (PR) under interference from the ST. In the subsequent communication phases, the next URs apply the decode-and-forward technique to transmit the signals. In the last communication phase, the Internet of Things destinations (IDs) receive their signals in the presence of an eavesdropper (EAV). Accordingly, the outage probability of the primary network, the throughput of the secondary network, and the leakage probability at the EAV are analyzed. On this basis, we propose a hybrid search method combining particle swarm optimization (PSO) and continuous genetic algorithm (CGA) to optimize the UR configurations and the NOMA power allocation to maximize the throughput of the secondary network under performance and security constraints

Measurement of neutron flux and gamma dose rate distribution inside a water phantom for Boron Neutron Capture Therapy study at Dalat Research Reactor

Exposure dose rate to the tumor and surrounding cells during neutron beam irradiation in Boron Neutron Capture Therapy (BNCT) comes not only from heavy charged particles produced from the 10B(n,α)7Li nuclear reaction, but also from neutron-induced reactions with other biological elements in living tissue, as well as from gamma rays leaked from the reactor core. At Dalat Research Reactor, Vietnam, the neutron and gamma dose rate distribution inside a water phantom were measured by using activation method and Thermoluminescent Dosimeter (TLD) detector, respectively. The results showed that effective thermal neutron dose rate along the center line of the water phantom had a maximum value of 479 mSv h-1 at 1 cm in phantom and then decreases rapidly to 4.87 mSv h-1 at 10 cm. The gamma dose rate along the center line of the water phantom also reach its maximum of 4.31 mSv h-1 at 1 cm depth and decreases to 1.16 mSv h-1 at 10 cm position. The maximum biological tumor dose rate was 1.74 Gy-eq h-1, not high enough to satisfy the treatment requirement of brain tumors. However, the results of this work are important in supporting of BNCT study in the upcoming stages at Dalat Research Reactor

UAV based satellite-terrestrial systems with hardware impairment and imperfect SIC: Performance analysis of user pairs

We investigated the outage performance of non-orthogonal multiple access (NOMA) in satellite-terrestrial systems which contain hardware impairments. An unmanned aerial vehicle (UAV) was implemented to forward signals from a satellite to users on the ground. A two-user model was applied to achieve spectral efficiency. In practical, real-life scenarios, the UAV and ground users encounter issues with imperfect hardware. We examined the performance gap between two users experiencing practical problems such as hardware impairment and imperfect successive interference cancellation (SIC). To implement a practical scenario, Shadow-Rician fading was adopted in the satellite links, and Rician fading was employed in the terrestrial links for ground users. In the main results, we derived the closed-form expression of the outage probability, and to evaluate the system performance of two NOMA users, we obtained the approximate expressions for high signal-to-noise ratios (SNR). Finally, we produced Monte-Carlo simulations to verify the analytical expressions and demonstrate the effect of the main system parameters, such as the number of transmit antennas on the satellite, transmit SNR, and level of hardware impairment on the system performance metric.Web of Science911793711792

Impact of CCI on performance analysis of downlink satellite-terrestrial systems: outage probability and ergodic capacity perspective

The evolution of non-orthogonal multiple access (NOMA) has raised many opportunities for massive connectivity with less latency in signal transmissions at great distances. We aim to integrate NOMA with a satellite communications network to evaluate system performance under the impacts of imperfect channel state information and co-channel interference from nearby systems. In our considered system, two users perform downlink communications under power-domain NOMA. We analyzed the performance of this system with two modes of shadowing effect: heavy shadowing and average shadowing. The detailed performance was analyzed in terms of the outage probability and ergodic capacity of the system. We derive closed-form expressions and performed a numerical analysis. We discover that the performance of two destinations depends on the strength of the transmit power at the satellite. However, floor outage occurs because the system depends on other parameters, such as satellite link modes, noise levels, and the number of interference sources. To verify the authenticity of the derived closed-form expressions, we also perform Monte-Carlo simulations.Web of Science20221art. no. 7