Sum-rate maximisation comparison using incremental approaches with different constraints

In this work, the problem of rate maximisation of multichannel systems is considered. Two greedy allocation approaches using power (GPA) and bit (GBA) loading schemes with a slight difference in design constraints that aiming to maximise the overall system throughput are compared. Both algorithms use incremental bit loading whereby, the GPA is designed with main interest of efficient power utilisation. Whereas, the GBA sacrifices power utilisation to another design issue of achieving an average bit error ratio (BER) less than the target BER. Simulation results shows that with GPA algorithm better throughput is gained over the GBA algorithm while the latter guaranteed less BER

Trade-off between complexity and BER performance of a polynomial SVD-based broadband MIMO transceiver

In this paper we investigate non-linear precoding solutions for the problem of broadband multiple-input multiple output(MIMO) systems. Based on a polynomial singular value decomposition (PSVD) we can decouple a broadband MIMO channel into independent dispersive spectrally majorised single-input single-output (SISO) subchannels. In this contribution, the focus of our work is to explore the influence of approximations on the PSVD, and the performance degradation that can be expected as a result

Reduced complexity schemes to greedy power allocation for multicarrier systems

Discrete bit loading for multicarrier systems based on the greedy power allocation (GPA) algorithm is considered in this paper. A new suboptimal scheme that independently performs GPA on groups of subcarriers and therefore can significantly reduce complexity compared to the standard GPA is proposed. These groups are formed in an initial step of a uniform power allocation (UPA) algorithm. In order to more efficiently allocate the available transmit power, two power re-distribution algorithms are further introduced by including a transfer of residual power between groups. Simulation results show that the two proposed algorithms can achieve near optimal performance in two separate and distinctive SNR regions. We demonstrate by analysis how these methods can greatly simplify the computational complexity of the GPA algorithm

Greedy power allocation for multicarrier systems with reduced complexity

In this paper we consider a reduced complexity discrete bit loading for Multicarrier systems based on the greedy power allocation (GPA) under the constraints of transmit power budget, target BER, and maximum permissible QAM modulation order. Compared to the standard GPA, which is optimal in terms of maximising the data throughput, three suboptimal schemes are proposed, which perform GPA on subsets of subcarriers only. These subsets are created by considering the minimum SNR boundaries of QAM levels for a given BER. We demonstrate how these schemes can reduce complexity. Two of the proposed algorithms can achieve near optimal performance by including a transfer of residual power between groups at the expense of a very small extra cost. It is shown that the two near optimal schemes,while greatly reducing complexity, perform best in two separate and distinct SNR regions

Incremental rate maximisation power loading with BER improvements

This paper aims to maximise the rate over a MIMO link using incremental power and bit allocation. Two different schemes, greedy power allocation (GPA) and greedy bit allocation (GBA), are addressed and compared with the standard uniform power allocation (UPA). The design is constrained by the target BER, the total power budget, and fixed discrete modulation orders. We demonstrate through simulations that GPA outperforms GBA in terms of throughput and power conservation,while GBA is advantageouswhen a lower BER is beneficial. Once the design constraints are satisfied, remaining power is utilised in two possible ways, leading to improved performance of GPA and UPA algorithms. This redistribution is analysed for fairness in BER performance across all active subchannels using a bisection method

Dynamic Geospatial Spectrum Modelling: Taxonomy, Options and Consequences

Much of the research in Dynamic Spectrum Access (DSA) has focused on opportunistic access in the temporal domain. While this has been quite useful in establishing the technical feasibility of DSA systems, it has missed large sections of the overall DSA problem space. In this paper, we argue that the spatio-temporal operating context of specific environments matters to the selection of the appropriate technology for learning context information. We identify twelve potential operating environments and compare four context awareness approaches (on-board sensing, databases, sensor networks, and cooperative sharing) for these environments. Since our point of view is overall system cost and efficiency, this analysis has utility for those regulators whose objectives are reducing system costs and enhancing system efficiency. We conclude that regulators should pay attention to the operating environment of DSA systems when determining which approaches to context learning to encourage

The Moon Was Dreaming

https://digitalcommons.library.umaine.edu/mmb-vp-copyright/8217/thumbnail.jp

Parental factors associated with the decision to participate in a neonatal clinical trial

Importance: It remains poorly understood how parents decide whether to enroll a child in a neonatal clinical trial. This is particularly true for parents from racial or ethnic minority populations. Understanding factors associated with enrollment decisions may improve recruitment processes for families, increase enrollment rates, and decrease disparities in research participation. Objective: To assess differences in parental factors between parents who enrolled their infant and those who declined enrollment for a neonatal randomized clinical trial. Design, Setting, and Participants: This survey study conducted from July 2017 to October 2019 in 12 US level 3 and 4 neonatal intensive care units included parents of infants who enrolled in the High-dose Erythropoietin for Asphyxia and Encephalopathy (HEAL) trial or who were eligible but declined enrollment. Data were analyzed October 2019 through July 2020. Exposure: Parental choice of enrollment in neonatal clinical trial. Main Outcomes and Measures: Percentages and odds ratios (ORs) of parent participation as categorized by demographic characteristics, self-assessment of child\u27s medical condition, study comprehension, and trust in medical researchers. Survey questions were based on the hypothesis that parents who enrolled their infant in HEAL differ from those who declined enrollment across 4 categories: (1) infant characteristics and parental demographic characteristics, (2) perception of infant\u27s illness, (3) study comprehension, and (4) trust in clinicians and researchers. Results: Of a total 387 eligible parents, 269 (69.5%) completed the survey and were included in analysis. This included 183 of 242 (75.6%) of HEAL-enrolled and 86 of 145 (59.3%) of HEAL-declined parents. Parents who enrolled their infant had lower rates of Medicaid participation (74 [41.1%] vs 47 [55.3%]; P = .04) and higher rates of annual income greater than $55 000 (94 [52.8%] vs 30 [37.5%]; P = .03) compared with those who declined. Black parents had lower enrollment rates compared with White parents (OR, 0.35; 95% CI, 0.17-0.73). Parents who reported their infant\u27s medical condition as more serious had higher enrollment rates (OR, 5.7; 95% CI, 2.0-16.3). Parents who enrolled their infant reported higher trust in medical researchers compared with parents who declined (mean [SD] difference, 5.3 [0.3-10.3]). There was no association between study comprehension and enrollment. Conclusions and Relevance: In this study, the following factors were associated with neonatal clinical trial enrollment: demographic characteristics (ie, race/ethnicity, Medicaid status, and reported income), perception of illness, and trust in medical researchers. Future work to confirm these findings and explore the reasons behind them may lead to strategies for better engaging underrepresented groups in neonatal clinical research to reduce enrollment disparities