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Federated Knowledge Distillation
[article]
2020
arXiv
pre-print
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1 Federated Knowledge Distillation
Hyowoon Seoa , Jihong Parkb , Seungeun Ohc , ...
Park is with the School of Information Technology, Deakin University, Geelong, VIC
3220, Australia (email: jihong.park@deakin.edu.au). ...
arXiv:2011.02367v1
fatcat:625t67zzv5gvph2ntlie3oc2yq
Semantics-Native Communication with Contextual Reasoning
[article]
2021
arXiv
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Spurred by a huge interest in the post-Shannon communication, it has recently been shown that leveraging semantics can significantly improve the communication effectiveness across many tasks. In this article, inspired by human communication, we propose a novel stochastic model of System 1 semantics-native communication (SNC) for generic tasks, where a speaker has an intention of referring to an entity, extracts the semantics, and communicates its symbolic representation to a target listener. To
arXiv:2108.05681v1
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... further reach its full potential, we additionally infuse contextual reasoning into SNC such that the speaker locally and iteratively self-communicates with a virtual agent built on the physical listener's unique way of coding its semantics, i.e., communication context. The resultant System 2 SNC allows the speaker to extract the most effective semantics for its listener. Leveraging the proposed stochastic model, we show that the reliability of System 2 SNC increases with the number of meaningful concepts, and derive the expected semantic representation (SR) bit length which quantifies the extracted effective semantics. It is also shown that System 2 SNC significantly reduces the SR length without compromising communication reliability.
Towards Enabling Critical mMTC: A Review of URLLC within mMTC
[article]
2020
arXiv
pre-print
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Park is with Electronics and Telecommunications Research Institute, Daejeon, South Korea. ...
Park and J. Choi are with the School of Information Technology, Deakin University, Geelong, VIC 3220, Australia. Ok-S. ...
arXiv:2007.08793v1
fatcat:khi7rve3evbtvpglik3fo3pqdy
Revitalizing Copybacks in Modern SSDs: Why and How
[article]
2018
arXiv
pre-print
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For modern flash-based SSDs, the performance overhead of internal data migrations is dominated by the data transfer time, not by the flash program time as in old SSDs. In order to mitigate the performance impact of data migrations, we propose rCopyback, a restricted version of copyback. Rcopyback works like the original copyback except that only n consecutive copybacks are allowed. By limiting the number of successive copybacks, it guarantees that no data reliability problem occurs when data is
arXiv:1810.04603v1
fatcat:4xxmg3ycx5f6jddry7lvalokgq
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... internally migrated using rCopyback. In order to take a full advantage of rCopyback, we developed a rCopyback-aware FTL, rcFTL, which intelligently decides whether rCopyback should be used or not by exploiting varying host workloads. Our evaluation results show that rcFTL can improve the overall I/O throughput by 54% on average over an existing FTL which does not use copybacks.
Performance Analysis of Blockchain Systems with Wireless Mobile Miners
[article]
2019
arXiv
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In this paper, a novel framework that uses wireless mobile miners (MMs) for computation purposes in a blockchain system is proposed. In the introduced system, the blockchain ledger is located at the communication nodes (CNs), and the MMs associated with CNs process the blockchain's proof-of-work (PoW) computation to verify the originality of the data. The MM that is the first to finish its PoW will receive a reward by sending its computing result to the CNs that are connected to other MMs. In
arXiv:1906.06759v1
fatcat:hwer6zymsved3ceifgh4n4gm3m
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... e considered scenario, a blockchain forking event occurs if the MM having the shortest PoW delay fails to be the first to update its computing result to other MMs. To enable such mobile operations for a blockchain with minimum forking events, it is imperative to maintain low-latency wireless communications between MMs and CNs. To analyze the sensitivity of the system to latency, the probability of occurrence of a forking event is theoretically derived. The system is then designed so as to compute the forked block's PoW again to recover from a forking event. For this case, the average energy consumption of an MM is derived as a function of the system parameters such as the number of MMs and power consumed by the computing, transmission, and mobility processes of the MMs. Simulation results verify the analytical derivations and show that using a larger number of MMs can reduce the energy consumption by up to 94.5% compared to a blockchain system with a single MM.
Towards Enabling Critical mMTC: A Review of URLLC within mMTC
2020
IEEE Access
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Remote UAV Online Path Planning via Neural Network Based Opportunistic Control
[article]
2019
arXiv
pre-print
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This letter proposes a neural network (NN) aided remote unmanned aerial vehicle (UAV) online control algorithm, coined oHJB. By downloading a UAV's state, a base station (BS) trains an HJB NN that solves the Hamilton-Jacobi-Bellman equation (HJB) in real time, yielding the optimal control action. Initially, the BS uploads this control action to the UAV. If the HJB NN is sufficiently trained and the UAV is far away, the BS uploads the HJB NN model, enabling to locally carry out control decisions
arXiv:1910.04969v1
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... even when the connection is lost. Simulations corroborate the effectiveness of oHJB in reducing the UAV's travel time and energy by utilizing the trade-off between uploading delays and control robustness in poor channel conditions.
Communication-Efficient and Personalized Federated Lottery Ticket Learning
[article]
2021
arXiv
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The lottery ticket hypothesis (LTH) claims that a deep neural network (i.e., ground network) contains a number of subnetworks (i.e., winning tickets), each of which exhibiting identically accurate inference capability as that of the ground network. Federated learning (FL) has recently been applied in LotteryFL to discover such winning tickets in a distributed way, showing higher accuracy multi-task learning than Vanilla FL. Nonetheless, LotteryFL relies on unicast transmission on the downlink,
arXiv:2104.12501v1
fatcat:pyox62apbvaghkyouww3xqkf54
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... nd ignores mitigating stragglers, questioning scalability. Motivated by this, in this article we propose a personalized and communication-efficient federated lottery ticket learning algorithm, coined CELL, which exploits downlink broadcast for communication efficiency. Furthermore, it utilizes a novel user grouping method, thereby alternating between FL and lottery learning to mitigate stragglers. Numerical simulations validate that CELL achieves up to 3.6% higher personalized task classification accuracy with 4.3x smaller total communication cost until convergence under the CIFAR-10 dataset.
When Wireless Communications Meet Computer Vision in Beyond 5G
[article]
2020
arXiv
pre-print
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This article articulates the emerging paradigm, sitting at the confluence of computer vision and wireless communication, to enable beyond-5G/6G mission-critical applications (autonomous/remote-controlled vehicles, visuo-haptic VR, and other cyber-physical applications). First, drawing on recent advances in machine learning and the availability of non-RF data, vision-aided wireless networks are shown to significantly enhance the reliability of wireless communication without sacrificing spectral
arXiv:2010.06188v1
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... fficiency. In particular, we demonstrate how computer vision enables look-ahead prediction in a millimeter-wave channel blockage scenario, before the blockage actually happens. From a computer vision perspective, we highlight how radio frequency (RF) based sensing and imaging are instrumental in robustifying computer vision applications against occlusion and failure. This is corroborated via an RF-based image reconstruction use case, showcasing a receiver-side image failure correction resulting in reduced retransmission and latency. Taken together, this article sheds light on the much-needed convergence of RF and non-RF modalities to enable ultra-reliable communication and truly intelligent 6G networks.
Tractable Resource Management in Millimeter-Wave Overlaid Ultra-Dense Cellular Networks
[article]
2015
arXiv
pre-print
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What does millimeter-wave (mmW) seek assistance for from micro-wave (μW) in a mmW overlaid 5G cellular network? This paper raises the question of whether to complement downlink (DL) or uplink (UL) transmissions, and concludes that μW should aid UL more. Such dedication to UL results from the low mmW UL rate due to high peak-to-average power ratio (PAPR) at mobile users. The DL/UL allocations are tractably provided based on a novel closed-form mm-μW spectral efficiency (SE) derivation via
arXiv:1507.04658v1
fatcat:qp5j7zzamrdkzaieqitpbqj3gm
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... tic geometry. The findings explicitly indicate: (i) both DL/UL mmW (or μW) SEs coincidentally converge on the same value in an ultra-dense cellular network (UDN) and (ii) such a mmW (or μW) UDN SE is a logarithmic function of BS-to-user density ratio. The corresponding mm-μW resource management is evaluated by utilizing a three dimensional (3D) blockage model with real geography in Seoul, Korea.
A Deep Learning-Based Fragment Detection Approach for the Arena Fragmentation Test
2020
Applied Sciences
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The arena fragmentation test (AFT) is one of the tests used to design an effective warhead. Conventionally, complex and expensive measuring equipment is used for testing a warhead and measuring important factors such as the size, velocity, and the spatial distribution of fragments where the fragments penetrate steel target plates. In this paper, instead of using specific sensors and equipment, we proposed the use of a deep learning-based object detection algorithm to detect fragments in the
doi:10.3390/app10144744
fatcat:xfwdomt6hzhnvhgj4d4uinzu74
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... To this end, we acquired many high-speed videos and built an AFT image dataset with bounding boxes of warhead fragments. Our method fine-tuned an existing object detection network named the Faster R-convolutional neural network (CNN) on this dataset with modification of the network's anchor boxes. We also employed a novel temporal filtering method, which was demonstrated as an effective non-fragment filtering scheme in our recent previous image processing-based fragment detection approach, to capture only the first penetrating fragments from all detected fragments. We showed that the performance of the proposed method was comparable to that of a sensor-based system under the same experimental conditions. We also demonstrated that the use of deep learning technologies in the task of AFT significantly enhanced the performance via a quantitative comparison between our proposed method and our recent previous image processing-based method. In other words, our proposed method outperformed the previous image processing-based method. The proposed method produced outstanding results in terms of finding the exact fragment positions.
Extreme URLLC: Vision, Challenges, and Key Enablers
[article]
2020
arXiv
pre-print
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Park than those set in 5G URLLC. In particular, high-precision robot control and autonomous vehicles cannot afford 5-nine reliability and millisecond latency [4] . ...
arXiv:2001.09683v1
fatcat:shkrmqtzzvfzzgnuvrgta464zq
Downlink Performance of Dense Antenna Deployment: To Distribute or Concentrate?
[article]
2019
arXiv
pre-print
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Massive multiple-input multiple-output (massive MIMO) and small cell densification are complementary key 5G enablers. Given a fixed number of the entire base-station antennas per unit area, this paper fairly compares (i) to deploy few base stations (BSs) and concentrate many antennas on each of them, i.e. massive MIMO, and (ii) to deploy more BSs equipped with few antennas, i.e. small cell densification. We observe that small cell densification always outperforms for both signal-to-interference
arXiv:1910.08868v1
fatcat:yhbol45a3femdiyvmmyjo2xzve
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... ratio (SIR) coverage and energy efficiency (EE), when each BS serves multiple users via L number of sub-bands (multi-carrier transmission). Moreover, we also observe that larger L increases SIR coverage while decreasing EE, thus urging the necessity of optimal 5G network design. These two observations are based on our novel closed-form SIR coverage probability derivation using stochastic geometry, also validated via numerical simulations.
Revisiting Frequency Reuse towards Supporting Ultra-Reliable Ubiquitous-Rate Communication
[article]
2017
arXiv
pre-print
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One of the goals of 5G wireless systems stated by the NGMN alliance is to provide moderate rates (50+ Mbps) everywhere and with very high reliability. We term this service Ultra-Reliable Ubiquitous-Rate Communication (UR2C). This paper investigates the role of frequency reuse in supporting UR2C in the downlink. To this end, two frequency reuse schemes are considered: user-specific frequency reuse (FRu) and BS-specific frequency reuse (FRb). For a given unit frequency channel, FRu reduces the
arXiv:1703.04838v1
fatcat:ufb266m4pvez5linwl5gb2z7ea
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... ber of serving user equipments (UEs), whereas FRb directly decreases the number of interfering base stations (BSs). This increases the distance from the interfering BSs and the signal-to-interference ratio (SIR) attains ultra-reliability, e.g. 99% SIR coverage at a randomly picked UE. The ultra-reliability is, however, achieved at the cost of the reduced frequency allocation, which may degrade overall downlink rate. To fairly capture this reliability-rate tradeoff, we propose ubiquitous rate defined as the maximum downlink rate whose required SIR can be achieved with ultra-reliability. By using stochastic geometry, we derive closed-form ubiquitous rate as well as the optimal frequency reuse rules for UR2C.
A Unified View on Semantic Information and Communication: A Probabilistic Logic Approach
[article]
2022
arXiv
pre-print
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This article aims to provide a unified and technical approach to semantic information, communication, and their interplay through the lens of probabilistic logic. To this end, on top of the existing technical communication (TC) layer, we additionally introduce a semantic communication (SC) layer that exchanges logically meaningful clauses in knowledge bases. To make these SC and TC layers interact, we propose various measures based on the entropy of a clause in a knowledge base. These measures
arXiv:2201.05936v1
fatcat:52ctlrdokzbqblaw4dbfddmzia
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... llow us to delineate various technical issues on SC such as a message selection problem for improving the knowledge at a receiver. Extending this, we showcase selected examples in which SC and TC layers interact with each other while taking into account constraints on physical channels.
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