Limited Feedback Techniques in Multiple Antenna Wireless Communication Systems

Multiple antenna systems provide spatial multiplexing and diversity benefits.These systems also offer beamforming and interference mitigation capabilities in single-user (SU) and multi-user (MU) scenarios, respectively. Although diversity can be achieved without channel state information (CSI) at the transmitter using space-time codes, the knowledge of instantaneous CSI at the transmitter is essential to the above mentioned gains. In frequency division duplexing (FDD) systems, limited feedback techniques are employed to obtain CSI at the transmitter from the receiver using a low-rate link. As a consequence, CSI acquired by the transmitter in such manner have errors due to channel estimation and codebook quantization at the receiver, resulting in performance degradation of multi-antenna systems. In this thesis, we examine CSI inaccuracies due to codebook quantization errors and investigate several other aspects of limited feedback in SU, MU and multicell wireless communication systems with various channel models. For SU multiple-input multiple-output (MIMO) systems, we examine the capacity loss using standard codebooks. In particular, we consider single-stream and two-stream MIMO transmissions and derive capacity loss expressions in terms of minimum squared chordal distance for various MIMO receivers. Through simulations, we investigate the impact of codebook quantization errors on the capacity performance in uncorrelated Rayleigh, spatially correlated Rayleigh and standardized MIMO channels. This work motivates the need of effective codebook design to reduce the codebook quantization errors in correlated channels. Subsequently, we explore the improvements in the design of codebooks in temporally and spatially correlated channels for MU multiple-input single-output (MISO) systems, by employing scaling and rotation techniques. These codebooks quantize instantaneous channel direction information (CDI) and are referred as differential codebooks in the thesis. We also propose various adaptive scaling techniques for differential codebooks where packing density of codewords in the differential codebook are altered according to the channel condition, in order to reduce the quantization errors. The proposed differential codebooks improve the spectral efficiency of the system by minimizing the codebook quantization errors in spatially and temporally correlated channels. Later, we broaden the scope to massive MISO systems and propose trellis coded quantization (TCQ) schemes to quantize CDI. Unlike conventional codebook approach, the TCQ scheme does not require exhaustive search to select an appropriate codeword, thus reducing computational complexity and memory requirement at the receiver. The proposed TCQ schemes yield significant performance improvements compared to the existing TCQ based limited feedback schemes in both temporally and spatially correlated channels. Finally, we investigate interference coordination for multicell MU MISO systems using regularized zero-forcing (RZF) precoding. We consider random vector quantization (RVQ) codebooks and uncorrelated Rayleigh channels. We derive expected SINR approximations for perfect CDI and RVQ codebook-based CDI. We also propose an adaptive bit allocation scheme which aims to minimize the network interference and moreover, improves the spectral efficiency compared to equal bit allocation and coordinated zero-forcing (ZF) based adaptive bit allocation schemes

Role of Corporate Social Responsibility in Corporate Reputation via Organizational Trust and Commitment

Purpose: The conceptual framework based on a comprehensive literature review hypothesized that the perceived CSR of an organization may lead to the development of trust and commitment among the employees, which in turn may lead to the building of the corporate reputation of the organization. Along with that, the moderating effects of HRM practices and organizational justice have also been investigated in the given relationship of corporate social responsibility (CSR) with its given mediators i.e., “organizational trust” and “organizational commitment”. Methodology: The target population of the study was comprised of the management and teaching faculty of educational institutions. The random sampling technique was employed to carry out an empirical study of 380 samples of employees. The data collected were analyzed by using the Smart PLS 3 software. The model was tested, and all the hypotheses were accepted. Findings: A positive relationship has been observed between CSR investments and corporate reputation. Furthermore, empirical results show that the employee’s commitment and the level of trust towards the organization serve as partial mediators between CSR practices and corporate reputation. The results showed that all of the hypotheses were accepted. Conclusion: This study is intuitive and empirically substantiates the selection of organizational justice and HRM practices as moderating variables between the observed CSR activities and its given mediators

Self-Navigation Car using Reinforcement Learning

In this paper, a project is described which is a 2-D modelled version of a car that will learn how to drive itself. It will have to figure everything out on its own. In addition, to achieve that the simulator contains a car running simultaneously &can be controlled by different control algorithms - heuristic, reinforcement learning-based, etc. For each dynamic input, the Reinforcement- Learning modifies new patterns. Ultimately, Reinforcement Learning helps in maximizing the reward from every state. In this first Part, we will implement a Reinforcement-Learning model to build an AI for Self Driving Car. Project will be focusing on the brain of the car not any graphics. The car will detect obstacles and take basic actions. To make autonomous car or self-driving car a reality, some of the factors to be considered are human safety and quality of life

On the performance of multiuser MIMO systems relying on full-duplex CSI acquisition

IEEE In this paper, we propose a combined full duplex (FD) and half duplex (HD) based transmission and channel acquisition model for open-loop multiuser multiple-input multipleoutput (MIMO) systems. Assuming residual self interference (SI) at the BS, the idea is to utilize the FD mode during the uplink (UL) training phase in order to achieve simultaneous downlink (DL) data transmission and UL CSI acquisition. More specifically, the BS begins serving a user when its CSI becomes available, while at the same time, it also receives UL pilots from the next scheduled user. We investigate both zero-forcing (ZF) and maximum ratio transmission (MRT) MIMO beamforming techniques for the DL data transmission in the FD mode. The BS switches to the HD mode once it receives the CSI of all users and it employs ZF beamforming for the DL data transmission until the end of the transmission frame. Furthermore, we derive closedform approximations for the lower bounded ergodic achievable rate relying on the proposed model. Our numerical results show that the proposed FD-HD transmission and channel acquisition approach outperforms its conventional HD counterpart and achieves higher data rates

A Strategy for Classification of “Vaginal vs. Cesarean Section” Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings

We propose objective and robust measures for the purpose of classification of “vaginal vs. cesarean section” delivery by investigating temporal dynamics and complex interactions between fetal heart rate (FHR) and maternal uterine contraction (UC) recordings from cardiotocographic (CTG) traces. Multivariate extension of empirical mode decomposition (EMD) yields intrinsic scales embedded in UC-FHR recordings while also retaining inter-channel (UC-FHR) coupling at multiple scales. The mode alignment property of EMD results in the matched signal decomposition, in terms of frequency content, which paves the way for the selection of robust and objective time-frequency features for the problem at hand. Specifically, instantaneous amplitude and instantaneous frequency of multivariate intrinsic mode functions are utilized to construct a class of features which capture nonlinear and nonstationary interactions from UC-FHR recordings. The proposed features are fed to a variety of modern machine learning classifiers (decision tree, support vector machine, AdaBoost) to delineate vaginal and cesarean dynamics. We evaluate the performance of different classifiers on a real world dataset by investigating the following classifying measures: sensitivity, specificity, area under the ROC curve (AUC) and mean squared error (MSE). It is observed that under the application of all proposed 40 features AdaBoost classifier provides the best accuracy of 91.8% sensitivity, 95.5% specificity, 98% AUC, and 5% MSE. To conclude, the utilization of all proposed time-frequency features as input to machine learning classifiers can benefit clinical obstetric practitioners through a robust and automatic approach for the classification of fetus dynamics

Neurokinin 1 receptor antagonist along with dexamethasone reduces the inflammation in COVID-19 patients: a novel therapeutic approach

Background: Corona virus infection is a respiratory infection, compromising the normal breathing in critical patients by damaging the lungs. The aim of this study was to evaluate the clinical outcomes of Substance P receptor Neurokinin 1 antagonist in COVID-19 patients against the usual treatments as controls.Methods: It is a two-arm, open-label, randomized clinical trial that was carried out at Bahria International Hospital in Lahore, Pakistan. PCR-positive, hospitalized patients older than 18 years old, all sexes, and in the critical to life-threatening stage were included. 52 patients were placed in control group A and 67 patients were placed in intervention group B out of a total 119 patients who were randomly assigned to both arms. Before and after the intervention, lab tests were conducted in both groups. Aprepitant, a neurokinin-1 receptor antagonist, was additionally administered to the other arm while the other arm got standard therapy and care. Additionally, both groups received oral administration of the corticosteroid dexamethasone.Results: Patients in group A were on average 56.05 years old, compared to 58.1 years old in group B. There were 24 women in group A and 28 in group B, while there were 28 men and 39 women in group A. Group A had three critical cases, but group B had six. The reduction in C-reactive protein in the intervention group, improvement in platelet count in group B, and normalization of ferritin and LDH levels in group B all indicated decreased inflammation in the biochemical and haematological parameters in both groups. However, because of the reduced sample size, it wasn't very significant.Conclusion: The results of this recent trial provide a solid indication of Aprepitant's medicinal potential. Patients who got a combined therapy of dexamethasone and aprepitant had better clinical results, more favourable lab results, and lower levels of C-reactive protein, an inflammatory marker