Matrix Completion-Based Channel Estimation for MmWave Communication Systems With Array-Inherent Impairments

Hybrid massive MIMO structures with reduced hardware complexity and power consumption have been widely studied as a potential candidate for millimeter wave (mmWave) communications. Channel estimators that require knowledge of the array response, such as those using compressive sensing (CS) methods, may suffer from performance degradation when array-inherent impairments bring unknown phase errors and gain errors to the antenna elements. In this paper, we design matrix completion (MC)-based channel estimation schemes which are robust against the array-inherent impairments. We first design an open-loop training scheme that can sample entries from the effective channel matrix randomly and is compatible with the phase shifter-based hybrid system. Leveraging the low-rank property of the effective channel matrix, we then design a channel estimator based on the generalized conditional gradient (GCG) framework and the alternating minimization (AltMin) approach. The resulting estimator is immune to array-inherent impairments and can be implemented to systems with any array shapes for its independence of the array response. In addition, we extend our design to sample a transformed channel matrix following the concept of inductive matrix completion (IMC), which can be solved efficiently using our proposed estimator and achieve similar performance with a lower requirement of the dynamic range of the transmission power per antenna. Numerical results demonstrate the advantages of our proposed MC-based channel estimators in terms of estimation performance, computational complexity and robustness against array-inherent impairments over the orthogonal matching pursuit (OMP)-based CS channel estimator.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

MECHANISM FOR CROSS PROCESS COMMUNICATION WITH ANR CHECKING AND AUTOMATIC OBJECT SERIALIZATION

A host and a client may perform inter-process communication (IPC) with automatic object serialization and deserialization and with application not responding (ANR) checking. A sender (e.g., a host process, host application, host device, a client process, client application, client device, etc.) may automatically serialize an object by using reflection (e.g., the ability of a process to examine, introspect, and modify its own structure and behavior) to recursively obtain all fields and corresponding key-value pairs from each layer of the object (as well as any parent object with inheritance). The sender may bundle the fields and key-value pairs and transmit the bundle to a receiver (e.g., the client if the host is the sender, the host if the client is the sender, etc.) via a network. The receiver may examine the bundle and determine the type of the object (e.g., arrays, string classes, interfaces, etc.) based on the fields and/or the key-value pairs stored in the bundle. The receiver may then implement the appropriate process for reconstructing the object from the based on the fields and the corresponding key-pairs. This serialization and deserialization process may be performed for each communication transmitted between the sender and the receiver. Additionally or alternatively, when the sender communicates with the receiver, the sender may send a message via a binder that causes the receiver to send a generic callback to the sender. In some examples, if the sender does not receive the callback before a predetermined period expires (e.g., the call timed-out), the sender may output a notification that the receiver (e.g., the application executing at the receiver) is not responding. In this way, the binder may provide ANR checking that informs the user whether an error has occurred or not

Efficient execution of Java programs on GPU

Dissertação de mestrado em Informatics EngineeringWith the overwhelming increase of demand of computational power made by fields as Big Data, Deep Machine learning and Image processing the Graphics Processing Units (GPUs) has been seen as a valuable tool to compute the main workload involved. Nonetheless, these solutions have limited support for object-oriented languages that often require manual memory handling which is an obstacle to bringing together the large community of object oriented programmers and the high-performance computing field. In this master thesis, different memory optimizations and their impacts were studied in a GPU Java context using Aparapi. These include solutions for different identifiable bottlenecks of commonly used kernels exploiting its full capabilities by studying the GPU hardware and current techniques available. These results were set against common used C/OpenCL benchmarks and respective optimizations proving, that high-level languages can be a solution to high-performance software demand.Com o aumento de poder computacional requisitado por campos como Big Data, Deep Machine Learning e Processamento de Imagens, as unidades de processamento gráfico (GPUs) tem sido vistas como uma ferramenta valiosa para executar a principal carga de trabalho envolvida. No entanto, esta solução tem suporte limitado para linguagens orientadas a objetos. Frequentemente estas requerem manipulação manual de memória, o que é um obstáculo para reunir a grande comunidade de programadores orientados a objetos e o campo da computação de alto desempenho. Nesta dissertação de mestrado, diferentes otimizações de memória e os seus impactos foram estudados utilizando Aparapi. As otimizações estudadas pretendem solucionar bottle-necks identificáveis em kernels frequentemente utilizados. Os resultados obtidos foram comparados com benchmarks C / OpenCL populares e as suas respectivas otimizações, provando que as linguagens de alto nível podem ser uma solução para programas que requerem computação de alto desempenho

Semiconductor disk lasers: the future's bright; the colour's flexible

Presentation describing semiconductor disk lasers, their use and how they work

Gene expression profiling in slow-Type calf soleus muscle of 30 days space-flown mice

Microgravity exposure as well as chronic disuse are two main causes of skeletal muscle atrophy in animals and humans. The antigravity calf soleus is a reference postural muscle to investigate the mechanism of disuse-induced maladaptation and plasticity of human and rodent (rats or mice) skeletal musculature. Here, we report microgravity-induced global gene expression changes in space-flown mouse skeletal muscle and the identification of yet unknown disuse susceptible transcripts found in soleus (a mainly slow phenotype) but not in extensor digitorum longus (a mainly fast phenotype dorsiflexor as functional counterpart to soleus). Adult C57Bl/N6 male mice (n = 5) flew aboard a biosatellite for 30 days on orbit (BION-M1 mission, 2013), a sex and age-matched cohort were housed in standard vivarium cages (n = 5), or in a replicate flight habitat as ground control (n = 5). Next to disuse atrophy signs (reduced size and myofiber phenotype I to II type shift) as much as 680 differentially expressed genes were found in the space-flown soleus, and only 72 in extensor digitorum longus (only 24 genes in common) compared to ground controls. Altered expression of gene transcripts matched key biological processes (contractile machinery, calcium homeostasis, muscle development, cell metabolism, inflammatory and oxidative stress response). Some transcripts (Fzd9, Casq2, Kcnma1, Ppara, Myf6) were further validated by quantitative real-time PCR (qRT-PCR). Besides previous reports on other leg muscle types we put forth for the first time a complete set of microgravity susceptible gene transcripts in soleus of mice as promising new biomarkers or targets for optimization of physical countermeasures and rehabilitation protocols to overcome disuse atrophy conditions in different clinical settings, rehabilitation and spaceflight