• Title/Summary/Keyword: TD algorithm

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RAKE Receiver for Time Division Synchronous CDMA Mobile Terminal

  • Xiao Yang;Lee Kwang-Jae;Lee Moon-Ho
    • Journal of electromagnetic engineering and science
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    • v.6 no.1
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    • pp.10-17
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    • 2006
  • For the sake of the potential ability of overcoming interference in TD-SCDMA(time division-synchronous code division multiple access) systems, pilot signal is adopted, but the presented TD-SCDMA protocol has not considered the Rake technique for their mobile terminals. This paper developed a RAKE receiver algorithm and an implementation circuit, which make use of the pilot signal in the burst structure of the TD-SCDMA base station to estimate main channel parameter(channel delays) in the downlink of TD-SCDMA wireless network. The algorithm can reduce multipath interference for the mobile units in multiusers' case. Theoretic performance analysis presented in the paper and computer simulations show that there is a range of BER for Rake receiver and confirm that the proposed RAKE receiver algorithm achieved a better performance under multipath fading propagation and multiusers conditions.

Max-Mean N-step Temporal-Difference Learning Using Multi-Step Return (멀티-스텝 누적 보상을 활용한 Max-Mean N-Step 시간차 학습)

  • Hwang, Gyu-Young;Kim, Ju-Bong;Heo, Joo-Seong;Han, Youn-Hee
    • KIPS Transactions on Computer and Communication Systems
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    • v.10 no.5
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    • pp.155-162
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    • 2021
  • n-step TD learning is a combination of Monte Carlo method and one-step TD learning. If appropriate n is selected, n-step TD learning is known as an algorithm that performs better than Monte Carlo method and 1-step TD learning, but it is difficult to select the best values of n. In order to solve the difficulty of selecting the values of n in n-step TD learning, in this paper, using the characteristic that overestimation of Q can improve the performance of initial learning and that all n-step returns have similar values for Q ≈ Q*, we propose a new learning target, which is composed of the maximum and the mean of all k-step returns for 1 ≤ k ≤ n. Finally, in OpenAI Gym's Atari game environment, we compare the proposed algorithm with n-step TD learning and proved that the proposed algorithm is superior to n-step TD learning algorithm.

Duplication with Task Assignment in Mesh Distributed System

  • Sharma, Rashmi;Nitin, Nitin
    • Journal of Information Processing Systems
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    • v.10 no.2
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    • pp.193-214
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    • 2014
  • Load balancing is the major benefit of any distributed system. To facilitate this advantage, task duplication and migration methodologies are employed. As this paper deals with dependent tasks (DAG), we used duplication. Task duplication reduces the overall schedule length of DAG along-with load balancing. This paper proposes a new task duplication algorithm at the time of tasks assignment on various processors. With the intention of conducting proposed algorithm performance computation; simulation has been done on the Netbeans IDE. The mesh topology of a distributed system is simulated at this juncture. For task duplication, overall schedule length of DAG is the main parameter that decides the performance of a proposed duplication algorithm. After obtaining the results we compared our performance with arbitrary task assignment, CAWF and HEFT-TD algorithms. Additionally, we also compared the complexity of the proposed algorithm with the Duplication Based Bottom Up scheduling (DBUS) and Heterogeneous Earliest Finish Time with Task Duplication (HEFT-TD).

Data rate control for suppression of inter-cell interference in TD-SCDMA systems (TD-SCDMA에서 셀간 간섭 억제를 위한 전송속도 제어)

  • Yeo, Woon-Young;Lee, Sang-Yun
    • Proceedings of the IEEK Conference
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    • 2008.06a
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    • pp.265-266
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    • 2008
  • TD-SCDMA combines TDMA and CDMA components to provide more efficient use of radio resources. However, since the same frequency band is used in both the uplink and downlink, serious interference may occur if the base stations are not synchronized. The interference caused by different transmission directions between neighboring cells is called cross-slot interference. This paper proposes a data rate control algorithm that can decrease the cross-slot interference in TD-SCDMA.

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Fast-convergence trilinear decomposition algorithm for angle and range estimation in FDA-MIMO radar

  • Wang, Cheng;Zheng, Wang;Li, Jianfeng;Gong, Pan;Li, Zheng
    • ETRI Journal
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    • v.43 no.1
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    • pp.120-132
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    • 2021
  • A frequency diverse array (FDA) multiple-input multiple-output (MIMO) radar employs a small frequency increment across transmit elements to produce an angle-range-dependent beampattern for target angle and range detection. The joint angle and range estimation problem is a trilinear model. The traditional trilinear alternating least square (TALS) algorithm involves high computational load due to excessive iterations. We propose a fast-convergence trilinear decomposition (FC-TD) algorithm to jointly estimate FDA-MIMO radar target angle and range. We first use a propagator method to obtain coarse angle and range estimates in the data domain. Next, the coarse estimates are used as initialized parameters instead of the traditional TALS algorithm random initialization to reduce iterations and accelerate convergence. Finally, fine angle and range estimates are derived and automatically paired. Compared to the traditional TALS algorithm, the proposed FC-TD algorithm has lower computational complexity with no estimation performance degradation. Moreover, Cramer-Rao bounds are presented and simulation results are provided to validate the proposed FC-TD algorithm effectiveness.

Enhancing VANET Security: Efficient Communication and Wormhole Attack Detection using VDTN Protocol and TD3 Algorithm

  • Vamshi Krishna. K;Ganesh Reddy K
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.18 no.1
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    • pp.233-262
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    • 2024
  • Due to the rapid evolution of vehicular ad hoc networks (VANETs), effective communication and security are now essential components in providing secure and reliable vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. However, due to their dynamic nature and potential threats, VANETs need to have strong security mechanisms. This paper presents a novel approach to improve VANET security by combining the Vehicular Delay-Tolerant Network (VDTN) protocol with the Deep Reinforcement Learning (DRL) technique known as the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. A store-carry-forward method is used by the VDTN protocol to resolve the problems caused by inconsistent connectivity and disturbances in VANETs. The TD3 algorithm is employed for capturing and detecting Worm Hole Attack (WHA) behaviors in VANETs, thereby enhancing security measures. By combining these components, it is possible to create trustworthy and effective communication channels as well as successfully detect and stop rushing attacks inside the VANET. Extensive evaluations and simulations demonstrate the effectiveness of the proposed approach, enhancing both security and communication efficiency.

A Verification of the Numerical Energy Conservation Property of the FD-TD(Finite Difference-Time Domain) Method by Using a Plane Wave Analysis (평면파 해석을 이용한 시간영역-유한차분법의 수치적 에너지 보존성질의 증명)

  • Ihn-Seok Kim
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.7 no.4
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    • pp.320-327
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    • 1996
  • This paper presents that the lossy or amplification property of the Finite Difference-Time Domain(FD-TD) method based on the leap-frog scheme is theoretically verified by using a plane wave analysis. The basic algorithm of the FD-TD method is introduced in order to help understanding the analysis procedure. Since our analysis is formulated by the Von Neumann's approach, the stability inequality is also produced as an another outcome.

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Goal-Directed Reinforcement Learning System (목표지향적 강화학습 시스템)

  • Lee, Chang-Hoon
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.10 no.5
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    • pp.265-270
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    • 2010
  • Reinforcement learning performs learning through interacting with trial-and-error in dynamic environment. Therefore, in dynamic environment, reinforcement learning method like TD-learning and TD(${\lambda}$)-learning are faster in learning than the conventional stochastic learning method. However, because many of the proposed reinforcement learning algorithms are given the reinforcement value only when the learning agent has reached its goal state, most of the reinforcement algorithms converge to the optimal solution too slowly. In this paper, we present GDRLS algorithm for finding the shortest path faster in a maze environment. GDRLS is select the candidate states that can guide the shortest path in maze environment, and learn only the candidate states to find the shortest path. Through experiments, we can see that GDRLS can search the shortest path faster than TD-learning and TD(${\lambda}$)-learning in maze environment.

A Study on the Electron Energy Distribution Function in $SF_6+Ar$ Mixtures Gas used by MCS-BE Algorithm ($SF_6+Ar$ 혼합기체의 MCS-BE 알고리즘에 의한 전자에너지 분포함수)

  • Kim, Sang-Nam;Ha, Sung-Chul
    • Proceedings of the KIEE Conference
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    • 2000.07e
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    • pp.17-21
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    • 2000
  • A Study on the electron energy distribution function in $SF_6+Ar$ mixtures gas used by MCS-BE algorithm, the electron swam parameters in the 0.5% and 0.2% $SF_6+Ar$ mixtures are measured by time of flight method over the E/N(Td) range from 30 to 300(Td). A two-term approximation of the Boltzmann equation analysis and Monte Carlo simulation have been also used to study electron transport coefficients. The electron energy distribution function has been analysed in $SF_6$ gas and $SF_6+Ar$ mixtures at E/N : 200(Td) for a case of the equilibrium region in the mean electron energy. The measured results and the calculated results have been compared each other.

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