Optimal Solutions for Joint Beamforming and Antenna Selection: From Branch and Bound to Graph Neural Imitation Learning

This work revisits the joint beamforming (BF) and antenna selection (AS) problem, as well as its robust beamforming (RBF) version under imperfect channel state information (CSI). Such problems arise due to various reasons, e.g., the costly nature of the radio frequency (RF) chains and energy/resource-saving considerations. The joint (R)BF\&AS problem is a mixed integer and nonlinear program, and thus finding {\it optimal solutions} is often costly, if not outright impossible. The vast majority of the prior works tackled these problems using techniques such as continuous approximations, greedy methods, and supervised machine learning -- yet these approaches do not ensure optimality or even feasibility of the solutions. The main contribution of this work is threefold. First, an effective {\it branch and bound} (B\&B) framework for solving the problems of interest is proposed. Leveraging existing BF and RBF solvers, it is shown that the B\&B framework guarantees global optimality of the considered problems. Second, to expedite the potentially costly B\&B algorithm, a machine learning (ML)-based scheme is proposed to help skip intermediate states of the B\&B search tree. The learning model features a {\it graph neural network} (GNN)-based design that is resilient to a commonly encountered challenge in wireless communications, namely, the change of problem size (e.g., the number of users) across the training and test stages. Third, comprehensive performance characterizations are presented, showing that the GNN-based method retains the global optimality of B\&B with provably reduced complexity, under reasonable conditions. Numerical simulations also show that the ML-based acceleration can often achieve an order-of-magnitude speedup relative to B\&B

Quantized Radio Map Estimation Using Tensor and Deep Generative Models

Spectrum cartography (SC), also known as radio map estimation (RME), aims at crafting multi-domain (e.g., frequency and space) radio power propagation maps from limited sensor measurements. While early methods often lacked theoretical support, recent works have demonstrated that radio maps can be provably recovered using low-dimensional models -- such as the block-term tensor decomposition (BTD) model and certain deep generative models (DGMs) -- of the high-dimensional multi-domain radio signals. However, these existing provable SC approaches assume that sensors send real-valued (full-resolution) measurements to the fusion center, which is unrealistic. This work puts forth a quantized SC framework that generalizes the BTD and DGM-based SC to scenarios where heavily quantized sensor measurements are used. A maximum likelihood estimation (MLE)-based SC framework under a Gaussian quantizer is proposed. Recoverability of the radio map using the MLE criterion are characterized under realistic conditions, e.g., imperfect radio map modeling and noisy measurements. Simulations and real-data experiments are used to showcase the effectiveness of the proposed approach.Comment: 16 pages, 9 figure

Radiological and functional outcome of displaced mid-shaft clavicular fracture managed with open reduction and internal fixation with precountered anatomical clavicular locking plate: a prospective study

Background: Displaced mid-shaft clavicular fractures are treated by conservative methods which shows higher rate of malunion and non-union with suboptimal outcomes. Fracture fixation by pre-countered anatomical clavicular locking plate avoids these complications. This study aims to assess the radiological and functional outcome after open reduction and internal fixation by pre-countered anatomical clavicular locking plate.Methods: Fifty patients of mid-shaft clavicular fractures with age group of 18 to 60 years were treated with open reduction and internal fixation with precountered anatomical clavicular locking plate from, in the span of November 2018 to May 2020. All the patients were followed up for six months for the study. Final functional outcome was assessed in six months.Results: All the fractures united at the average time of 16.32±2.37 weeks. Mean Constant and Murley score was 96.0±5.20. The outcome was graded as excellent in 45 (90%), good in 4 (8%) and fair in 1 (2%) patients.Conclusions: Hence displaced mid-shaft clavicular fractures can be treated with by precountered anatomical clavicular locking plate

Graph Imitation Learning for Optimal Joint Beamforming and Antenna Selection

Transmit beamforming is an important technique employed to improve efficiency and signal quality in wireless communication systems by steering signals towards their in- tended users. It often arises jointly with the antenna selection problem due to various reasons, such as limited number of radio frequency (RF) chains and energy/resource effi- ciency considerations. The joint robust beamforming and antenna selection (RBF&AS) problem is a mixed integer nonlinear program. Due to the NP-hard combinatorial nature of this problem, majority of existing methods rely on various heuristics, e.g., continuous approximations, greedy search, and supervised machine learning. However, these heuris- tics do not guarantee the optimality (or even feasibility) of the considered problem. To address this issue, we design an effective branch-and-bound (B&B) based method that guarantees optimal solutions to the problem of interest. To avoid the potentially costly nature of the proposed B&B algorithm, a machine learning-based scheme is pro- posed that expedites the B&B search by skipping intermediate steps of the algorithm. The learning model is based on a graph neural network (GNN) that provides resilience to commonly encountered problems in wireless comunications, namely, the change of problem size (e.g., the number of users) across the training and test stages. Finally, we provide a comprehensive theoretical analysis, which shows the proposed GNN-based method can reduce the complexity of the B&B method while retaining global optimality under reasonable conditions. Extensive numerical simulations show that the proposed method can provide near-optimal solution with an order-of-magnitude speedup relative to the B&B

An Object-Oriented Architecture for Field Data Acquisition, Processing and Information Extraction

Software architecture was developed to automate site specific field data acquisition, processing, and geo-referenced crop plant parameters extraction. The architecture supported acquisition and processing of different data streams such as digital video for machine vision and digital serial communications of NMEA strings. The number of channels for data import could be easily expanded for multiple video, GPS, and other signal sources. The architecture was objectoriented and each component in the architecture was developed as a separate class. A key component of this architecture was a supervisor class, which communicated and coordinated the operations on all other classes. Based on this framework, early stage corn population estimation (ESCOPE) software was developed which grabs pre-recorded digital video from a vehicle-mounted camera, that was passed over corn rows, and acquires GPS strings which were modulated and recorded on the audio channel. A digital video (DV) capture class was written to acquire video using Microsoft’s DirectShow® technology which enables camera control and video acquisition using higher level hardware functions. After completion of software development, reusability and extensibility characteristics were demonstrated by adding a class to acquire images from the hard drive and also by deriving a new image analyzer class to extract an additional feature. The architecture forms a general framework for developing reusable and extensible software for field data sensing systems

Adolescents with disabilities and caregivers experience of COVID-19 in rural Nepal

Introduction: Intersecting vulnerabilities of disability, low socio-economic status, marginalization, and age indicate that adolescents with disabilities in low-and middle-income countries were uniquely affected by the COVID-19 pandemic. Yet, there has been limited research about their experience. We conducted participatory research with adolescents with disabilities in rural, hilly Nepal to explore their experience of the pandemic and inform understanding about how they can be supported in future pandemics and humanitarian emergencies. Methods: We used qualitative methods, purposively sampling adolescents with different severe impairments from two rural, hilly areas of Nepal. We collected data through semi-structured interviews with five girls and seven boys between the age of 11 and 17 years old. Interviews used inclusive, participatory, and arts-based methods to engage adolescents, support discussions and enable them to choose what they would like to discuss. We also conducted semi-structured interviews with 11 caregivers. Results: We found that adolescents with disabilities and their families experienced social exclusion and social isolation because of COVID-19 mitigation measures, and some experienced social stigma due to misconceptions about transmission of COVID-19 and perceived increased vulnerability of adolescents with disabilities to COVID-19. Adolescents who remained connected with their peers throughout lockdown had a more positive experience of the pandemic than those who were isolated from friends. They became disconnected because they moved away from those they could communicate with, or they had moved to live with relatives who lived in a remote, rural area. We found that caregivers were particularly fearful and anxious about accessing health care if the adolescent they cared for became ill. Caregivers also worried about protecting adolescents from COVID-19 if they themselves got ill, and about the likelihood that the adolescent would be neglected if the caregiver died. Conclusion: Contextually specific research with adolescents with disabilities to explore their experience of the pandemic is necessary to capture how intersecting vulnerabilities can adversely affect particular groups, such as those with disabilities. The participation of adolescents with disabilities and their caregivers in the development of stigma mitigation initiatives and strategies to meet their needs in future emergencies is necessary to enable an informed and inclusive response