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

New Insights on Choroidal Vascularity: A Comprehensive Topographic Approach

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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