91 research outputs found
Learning RL-Policies for Joint Beamforming Without Exploration: A Batch Constrained Off-Policy Approach
In this work, we consider the problem of network parameter optimization for
rate maximization. We frame this as a joint optimization problem of power
control, beam forming, and interference cancellation. We consider the setting
where multiple Base Stations (BSs) communicate with multiple user equipment
(UEs). Because of the exponential computational complexity of brute force
search, we instead solve this nonconvex optimization problem using deep
reinforcement learning (RL) techniques. Modern communication systems are
notorious for their difficulty in exactly modeling their behavior. This limits
us in using RL-based algorithms as interaction with the environment is needed
for the agent to explore and learn efficiently. Further, it is ill-advised to
deploy the algorithm in the real world for exploration and learning because of
the high cost of failure. In contrast to the previous RL-based solutions
proposed, such as deep-Q network (DQN) based control, we suggest an offline
model-based approach. We specifically consider discrete batch-constrained deep
Q-learning (BCQ) and show that performance similar to DQN can be achieved with
only a fraction of the data without exploring. This maximizes sample efficiency
and minimizes risk in deploying a new algorithm to commercial networks. We
provide the entire project resource, including code and data, at the following
link: https://github.com/Heasung-Kim/ safe-rl-deployment-for-5g.Comment: 10 pages, 8 figure
TinyTurbo: Efficient Turbo Decoders on Edge
In this paper, we introduce a neural-augmented decoder for Turbo codes called
TINYTURBO . TINYTURBO has complexity comparable to the classical max-log-MAP
algorithm but has much better reliability than the max-log-MAP baseline and
performs close to the MAP algorithm. We show that TINYTURBO exhibits strong
robustness on a variety of practical channels of interest, such as EPA and EVA
channels, which are included in the LTE standards. We also show that TINYTURBO
strongly generalizes across different rate, blocklengths, and trellises. We
verify the reliability and efficiency of TINYTURBO via over-the-air
experiments.Comment: 10 pages, 6 figures. Published at the 2022 IEEE International
Symposium on Information Theory (ISIT
Task-aware Distributed Source Coding under Dynamic Bandwidth
Efficient compression of correlated data is essential to minimize
communication overload in multi-sensor networks. In such networks, each sensor
independently compresses the data and transmits them to a central node due to
limited communication bandwidth. A decoder at the central node decompresses and
passes the data to a pre-trained machine learning-based task to generate the
final output. Thus, it is important to compress the features that are relevant
to the task. Additionally, the final performance depends heavily on the total
available bandwidth. In practice, it is common to encounter varying
availability in bandwidth, and higher bandwidth results in better performance
of the task. We design a novel distributed compression framework composed of
independent encoders and a joint decoder, which we call neural distributed
principal component analysis (NDPCA). NDPCA flexibly compresses data from
multiple sources to any available bandwidth with a single model, reducing
computing and storage overhead. NDPCA achieves this by learning low-rank task
representations and efficiently distributing bandwidth among sensors, thus
providing a graceful trade-off between performance and bandwidth. Experiments
show that NDPCA improves the success rate of multi-view robotic arm
manipulation by 9% and the accuracy of object detection tasks on satellite
imagery by 14% compared to an autoencoder with uniform bandwidth allocation
Training and Education Differentiated Training Education Application Theoretical Orientation Job
Abstract- In simple terms, training and development refers to the imparting of specific skills, abilities and knowledge to an employee. A formal definition of training & development is … it is any attempt to improve current or future employee performance by increasing an employee’s ability to perform through learning, usually by changing the employee’s attitude or increasing his or her skills and knowledge. The need for training & development is determined by the employee’s performance deficiency, computed as follows: Training & Development need = Standard performance – Actual performance. We can make a distinction among training, education and development. Such distinction enables us to acquire a better perspective about the meaning of the terms. Training, as was stated earlier, refers to the process of imparting specific skills. Education, on the other hand, is confined to theoretical learning in classrooms
Investigations on Stub-Based UWB-MIMO Antennas to Enhance Isolation Using Characteristic Mode Analysis
In this article, very compact 2 × 2 and 4 × 4 MIMO (Multiple-Input and Multiple output) antennas are designed with the help of Characteristics Mode Analysis to enhance isolation between the elements for UWB applications. The proposed antennas are designed with Characteristic Mode Analysis (CMA) to gain physical insight and also to analyze the dominant mode. To improve isolation and minimize the mutual coupling between radiating elements, elliptical shaped stubs are used. The dimensions of the 2 × 2 and 4 × 4 MIMO antennas are 0.29λ0 × 0.21λ0 (28 × 20 mm2) and 0.29λ0 × 0.42λ0 (28 × 40 mm2), respectively. These antennas cover the (3.1 GHz–13.75 GHz) UWB frequency band and maintain remarkable isolation of more than 25 dB for both 2 × 2 and 4 × 4 antennas. The impedance bandwidth of the proposed 4 × 4 MIMO antenna is 126.40% from 3.1 GHz to 13.75 GHz, including X-Band and ITU bands. The proposed 4 × 4 antenna has good radiation efficiency, with a value of more than 92.5%. The envelope correlation coefficient (ECC), diversity gain (DG), mean effective gain (MEG), and channel capacity loss (CCL) matrices of the 4 × 4 antenna are simulated and tested. The corresponding values are 0.0045, 9.982, −3.1 dB, and 0.39, respectively. The simulated results are validated with measured results and favorable agreements for both the 2 × 2 and 4 × 4 UWB-MIMO antennas
Dopamine-functionalized InP/ZnS quantum dots as fluorescence probes for the detection of adenosine in microfluidic chip
Seshadri Reddy Ankireddy, Jongsung Kim Department of Chemical and Biological Engineering, Gachon University, Seongnam, Gyeonggi-Do, South Korea Abstract: Microbeads are frequently used as solid supports for biomolecules such as proteins and nucleic acids in heterogeneous microfluidic assays. Chip-based, quantum dot (QD)-bead-biomolecule probes have been used for the detection of various types of DNA. In this study, we developed dopamine (DA)-functionalized InP/ZnS QDs (QDs-DA) as fluorescence probes for the detection of adenosine in microfluidic chips. The photoluminescence (PL) intensity of the QDs-DA is quenched by Zn2+ because of the strong coordination interactions. In the presence of adenosine, Zn2+ cations preferentially bind to adenosine, and the PL intensity of the QDs-DA is recovered. A polydimethylsiloxane-based microfluidic chip was fabricated, and adenosine detection was confirmed using QDs-DA probes. Keywords: quantum dots, photoluminescence, microfluidic chip, adenosine, mercaptopropionic acid, mercaptoundecanoic acid, PDM
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