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Journal articles on the topic 'Adaptive power allocation'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

Razmi, Shirin, and Naser Parhizgar. "Adaptive resources assignment in OFDM-based cognitive radio systems." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 3 (June 1, 2019): 1935. http://dx.doi.org/10.11591/ijece.v9i3.pp1935-1943.

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Spectrum efficiency of orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems can be improved by adaptive resources allocation. In resources allocation, transmission resources such as modulation level and transmission power are adaptively assigned based on channel variations. The goal of this paper is maximize the total transmission rate of secondary user (SU). Hence, we investigate adaptive power and modulation allocation to achieve this purpose. For power allocation, we investigate optimal and conventional methods and then introduce a novel suboptimal algorithm to calculate the transmission power of each subcarrier. In addition, for adaptive modulation, we consider two kinds of modulations including multi-quadrature amplitude modulation (MQAM) and multi-phase-shift keying (MPSK). Also, simulation results are indicated the performance of our algorithm.

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2

Gortz, N., and E. Bresch. "Source-adaptive power allocation for digital modulation." IEEE Communications Letters 7, no. 12 (December 2003): 569–71. http://dx.doi.org/10.1109/lcomm.2003.821312.

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3

Liu, Jue, Nan Sha, Weiwei Yang, Jia Tu, and Lianxin Yang. "Hierarchical Q-Learning Based UAV Secure Communication against Multiple UAV Adaptive Eavesdroppers." Wireless Communications and Mobile Computing 2020 (October 8, 2020): 1–15. http://dx.doi.org/10.1155/2020/8825120.

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In this paper, we investigate secure unmanned aerial vehicle (UAV) communication in the presence of multiple UAV adaptive eavesdroppers (AEs), where each AE can conduct eavesdropping or jamming adaptively by learning others’ actions for degrading the secrecy rate more seriously. The one-leader and multi-follower Stackelberg game is adopted to analyze the mutual interference among multiple AEs, and the optimal transmit powers are proven to exist under the existing conditions. Following that, a mixed-strategy Stackelberg Equilibrium based on finite and discretized power set is also derived and a hierarchical Q-learning based power allocation algorithm (HQLA) is proposed to obtain the optimal power allocation strategy of the transmitter. Numerical results show that secrecy performance can be degraded severely by multiple AEs and verify the availability of the optimal power allocation strategy. Finally, the effect of the eavesdropping cost on the AE’s attack mode strategies is also revealed.

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4

Liu, Wei, and Jing Min Tang. "Dynamic Subcarrier and Power Allocation Based on Nash Bargaining Solution in Symmetric Cooperative OFDMA Networks." Advanced Materials Research 187 (February 2011): 510–15. http://dx.doi.org/10.4028/www.scientific.net/amr.187.510.

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In this paper, subcarrier and power allocation are jointly considered in a three-node symmetric cooperation orthogonal frequency-division multiple access uplink system. With the help of Nash bargaining solution, the dynamic subcarrier allocation scheme and the adaptive power allocation scheme are proposed for joint optimization. The joint resource allocation is decomposed and solved by dynamic subcarrier allocation algorithm and adaptive power allocation algorithm. Simulation results show the effectiveness of the proposed cooperative scheme.

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5

Xu, Xiao Rong, Ai Ping Huang, Jian Rong Bao, and Hang Guan Shan. "Aadptive Subcarrier Allocation for Multiple Cognitive Users over Fading Channels." Advanced Materials Research 765-767 (September 2013): 647–52. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.647.

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In Cognitive Radio Network (CRN), where Primary User (PU) and multiple Secondary Users (SUs) wish to communicate with their corresponding receivers simultaneously over fading channels, spectrum utilization and efficient resource allocation are both significant points for CRN. Interference between PU and SUs should be eliminated in order to realize spectrum sharing. Multi-user resource allocation with the target of maximizing the spectral efficiency of SUs and satisfying the proportional rate constraint between SUs are proposed under the conditions of total SU interference constraint. An adaptive low-complexity suboptimal algorithm for subcarrier and power joint allocation is presented based on Rate Adaptive (RA) criterion, where adaptive subcarrier allocation is performed by assuming equal power distribution, while each subcarrier is assigned in accordance with subcarrier efficiency function. Moreover, linear water-filling algorithm for power allocation is applied within each subcarrier. Simulation results indicate that, with the proposed adaptive subcarrier allocation, spectral efficiency of multiple SUs is superior to traditional subcarrier power joint allocation algorithm. Low computational complexity and adaptive features make it available for implementation.

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6

Li, Yonghui, Branka Vucetic, Zhendong Zhou, and Mischa Dohler. "Distributed Adaptive Power Allocation for Wireless Relay Networks." IEEE Transactions on Wireless Communications 6, no. 3 (March 2007): 948–58. http://dx.doi.org/10.1109/twc.2007.05256.

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7

Dong, Zanyang, Tao Shang, Qian Li, and Tang Tang. "Adaptive Power Allocation Scheme for Mobile NOMA Visible Light Communication System." Electronics 8, no. 4 (March 29, 2019): 381. http://dx.doi.org/10.3390/electronics8040381.

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Recently, due to its higher spectral efficiency and enhanced user experience, non-orthogonal multiple access (NOMA) has been widely studied in visible light communication (VLC) systems. As a main concern in NOMA-VLC systems, the power allocation scheme greatly affects the tradeoff between the total achievable data rate and user fairness. In this context, our main aim in this work was to find a more balanced power allocation scheme. To this end, an adaptive power allocation scheme based on multi-attribute decision making (MADM), which flexibly chooses between conventional power allocation or inverse power allocation (IPA) and the optimal power allocation factor, has been proposed. The concept of IPA is put forward for the first time and proves to be beneficial to achieving a higher total achievable data rate at the cost of user fairness. Moreover, considering users’ mobility along certain trajectories, we derived a fitting model of the optimal power allocation factor. The feasibility of the proposed adaptive scheme was verified through simulation and the fitting model was approximated to be the sum of three Gaussian functions.

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8

Alhamad, Raed, and Hatem Boujemâa. "Optimal power allocation for CRN-NOMA systems with adaptive transmit power." Signal, Image and Video Processing 14, no. 7 (March 26, 2020): 1327–34. http://dx.doi.org/10.1007/s11760-020-01674-8.

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9

Xu, Xiao Rong, Jian Wu Zhang, Bao Yu Zheng, and Jun Rong Yan. "An Efficient Subcarrier Power Allocation Algorithm in Cognitive WSN." Advanced Materials Research 255-260 (May 2011): 2062–66. http://dx.doi.org/10.4028/www.scientific.net/amr.255-260.2062.

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In Cognitive Wireless Sensor Network (C-WSN), spectrum utilization and energy-efficiency are both significant items for the whole network. Interference between Primary User (PU) sensor and Secondary User (SU) sensor should be eliminated in order to realize spectrum sharing. In this paper, mathematical model of multi-carrier power allocation in cognitive OFDM is constructed. Multi-carrier power allocation based on rate adaptive criterion is proposed under the constraints of SUs’ power control. An efficient subcarrier power allocation algorithm based on adaptive water-filling is proposed. The improved algorithm could directly determine the sub-carriers that do not require additional power injection by rough estimation of water levels. Computational complexity of proposed algorithm could reduce rapidly. Meanwhile, theoretical derivation and numerical results both indicate that, with the proposed power allocation algorithm, SU’s spectral efficiency is superior to other traditional water-filling schemes, and the algorithm also has some adaptive features for practical implementation in C-WSN.

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10

Fan, Sen Quan, Yan Dong Huang, En Qing Xu, Kai Zhang, Hai Zhou Zhu, Hui Zhai, and Nan Jiang. "An Adaptive Power Allocation for Spatial Multiplexing in MIMO Channels." Advanced Materials Research 756-759 (September 2013): 4179–83. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.4179.

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This paper proposes an adaptive power allocation in multiple-input multiple-output (MIMO) spatial multiplexing slow fading channels, which improves spectral efficiency and reduces the complexity of systems at the same time. Under the constraint of the same signal-to-interference and noise ratio (SINR) at each receive antenna, an iteration formula of power allocation algorithm for transmit antennas is derived. Besides, for a certain channel gain matrix, the same SINR value at each receive antenna is proved to have the limit value. The simulations of the proposed adaptive power allocation show that the algorithm is effective and can reach the convergence fast.

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11

Goot, R., T. Trigano, S. Tapuchi, and J. Gavan. "Adaptive allocation of power transmission for high-altitude platforms." annals of telecommunications - annales des télécommunications 68, no. 1-2 (June 16, 2012): 107–13. http://dx.doi.org/10.1007/s12243-012-0303-1.

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12

Ermolova, Natalia, and Boris Makarevitch. "Low complexity adaptive power and subcarrier allocation for OFDMA." IEEE Transactions on Wireless Communications 6, no. 2 (February 2007): 433–37. http://dx.doi.org/10.1109/twc.2007.05232.

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13

Singh, Shailendra, Moloud Shahbazi, Konstantinos Pelechrinis, Karthikeyan Sundaresan, Srikanth V. Krishnamurthy, and Sateesh Addepalli. "Adaptive Sub-Carrier Level Power Allocation in OFDMA Networks." IEEE Transactions on Mobile Computing 14, no. 1 (January 2015): 28–41. http://dx.doi.org/10.1109/tmc.2014.2312716.

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14

Chaitanya, Tumula V. K., and Tho Le-Ngoc. "Energy-Efficient Adaptive Power Allocation for Incremental MIMO Systems." IEEE Transactions on Vehicular Technology 65, no. 4 (April 2016): 2820–27. http://dx.doi.org/10.1109/tvt.2015.2417500.

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15

Cheong Yui Wong, R. S. Cheng, K. B. Lataief, and R. D. Murch. "Multiuser OFDM with adaptive subcarrier, bit, and power allocation." IEEE Journal on Selected Areas in Communications 17, no. 10 (1999): 1747–58. http://dx.doi.org/10.1109/49.793310.

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16

Zhao, Yahong, Zhongpei Zhang, and Weiling Wu. "Multicarrier DS-CDMA with adaptive modulation and power allocation." Journal of Electronics (China) 20, no. 3 (May 2003): 177–82. http://dx.doi.org/10.1007/bf02687701.

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17

Yang, Ying. "Adaptive Resource Allocation for the Multi-User Multi-Carrier Networks." Advanced Materials Research 663 (February 2013): 722–25. http://dx.doi.org/10.4028/www.scientific.net/amr.663.722.

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In this paper, we investigate the problem of allocating transmission data rates to users in multi-user multi-carrier networks. The paper provides a general problem of power and rate allocation that the utility functions can be nonconcave and nondifferetiable. This constrained optimization problem turns utility maximization into nonconvex, which is well-known to be difficult. To solve this problem, the dual optimization is analyzed to decompose the original optimization problem, and then a simple bisection algorithm is proposed.

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18

Wang, Zhen-Yu, Hong-Yi Yu, and Da-Ming Wang. "Channel and Bit Adaptive Power Control Strategy for Uplink NOMA VLC Systems." Applied Sciences 9, no. 2 (January 9, 2019): 220. http://dx.doi.org/10.3390/app9020220.

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Non-orthogonal multiple access (NOMA) can be an effective solution to the limited bandwidth of light emitting diodes for visible light communication (VLC) systems to support multiuser communication. The current available works for NOMA VLC systems mainly concentrate on downlinks and the existing power allocation algorithms mainly focus on the channel state information and ignore the influence of transmitted signals. In this paper, we propose a channel and bit adaptive power control strategy for uplink NOMA VLC systems by jointly considering the channel state information and the transmission bit rate. Under this adaptive power control strategy, it is proved that the received signal at the photodiode (PD) receiver constitutes a sizeable pulse amplitude modulation constellation and low-complexity maximum likelihood detection is admitted. The simulation results indicate that our proposed adaptive power control strategy outperforms the gain ratio power allocation scheme, fixed power allocation scheme, and time division multiple access scheme.

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19

Lu, Tong-Yu, Ka Pui Chung, Wai-Yin Poon, and Siu Hung Cheung. "Response-adaptive treatment allocation for clinical studies with ordinal responses." Statistical Methods in Medical Research 29, no. 2 (March 7, 2019): 359–73. http://dx.doi.org/10.1177/0962280219834061.

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Ordinal responses are common in clinical studies. Although the proportional odds model is a popular option for analyzing ordered-categorical data, it cannot control the type I error rate when the proportional odds assumption fails to hold. The latent Weibull model was recently shown to be a superior candidate for modeling ordinal data, with remarkably better performance than the latent normal model when the data are highly skewed. In clinical trials with ordinal responses, a balanced design is common, with equal sample allocation for each treatment. However, a more ethical approach is to adopt a response-adaptive allocation scheme in which more patients receive the better treatment. In this paper, we propose the use of the doubly adaptive biased coin design to generate treatment allocations that benefit the trial participants. The proposed treatment allocation scheme not only allows more patients to receive the better treatment, it also maintains compatible test power for the comparison of treatment efficiencies. A clinical example is used to illustrate the proposed procedure.

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20

Ibrahim, Mohammed, and Haider AlSabbagh. "Adaptive OFDMA Resource Allocation using Modified Multi-Dimension Genetic Algorithm." Iraqi Journal for Electrical and Electronic Engineering 12, no. 1 (June 1, 2016): 103–13. http://dx.doi.org/10.37917/ijeee.12.1.11.

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A considerable work has been conducted to cope with orthogonal frequency division multiple access (OFDMA) resource allocation with using different algorithms and methods. However, most of the available studies deal with optimizing the system for one or two parameters with simple practical condition/constraints. This paper presents analyses and simulation of dynamic OFDMA resource allocation implementation with Modified Multi-Dimension Genetic Algorithm (MDGA) which is an extension for the standard algorithm. MDGA models the resource allocation problem to find the optimal or near optimal solution for both subcarrier and power allocation for OFDMA. It takes into account the power and subcarrier constrains, channel and noise distributions, distance between user's equipment (UE) and base stations (BS), user priority weight – to approximate the most effective parameters that encounter in OFDMA systems. In the same time multi dimension genetic algorithm is used to allow exploring the solution space of resource allocation problem effectively with its different evolutionary operators: multi dimension crossover, multi dimension mutation. Four important cases are addressed and analyzed for resource allocation of OFDMA system under specific operation scenarios to meet the standard specifications for different advanced communication systems. The obtained results demonstrate that MDGA is an effective algorithm in finding the optimal or near optimal solution for both of subcarrier and power allocation of OFDMA resource allocation.

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21

Sadr, Sanam, Alagan Anpalagan, and Kaamran Raahemifar. "Suboptimal Rate Adaptive Resource Allocation for Downlink OFDMA Systems." International Journal of Vehicular Technology 2009 (August 18, 2009): 1–10. http://dx.doi.org/10.1155/2009/891367.

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This paper aims to study the performance of low complexity adaptive resource allocation in the downlink of OFDMA systems with fixed or variable rate requirements (with fairness consideration). Two suboptimal resource allocation algorithms are proposed using the simplifying assumption of transmit power over the entire bandwidth. The objective of the first algorithm is to maximize the total throughput while maintaining rate proportionality among the users. The proposed suboptimal algorithm prioritizes the user with the highest sensitivity to the subcarrier allocation, and the variance over the subchannel gains is used to define the sensitivity of each user. The second algorithm concerns rate adaptive resource allocation in multiuser systems with fixed rate constraints. We propose a suboptimal joint subchannel and power allocation algorithm which prioritizes the users with the highest required data rates. The main feature of this algorithm is its low complexity while achieving the rate requirements.

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22

LI, Dong. "Joint Adaptive Modulation and Power Allocation in Cognitive Radio Networks." Int'l J. of Communications, Network and System Sciences 01, no. 03 (2008): 228–34. http://dx.doi.org/10.4236/ijcns.2008.13027.

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23

Shin, Joon-Ho, Dong-Geon Kim, and Hyung-Rae Park. "Efficient Power Allocation Algorithms for Adaptive Spatial Multiplexing MIMO Systems." Journal of Korea Information and Communications Society 36, no. 4C (April 30, 2011): 232–40. http://dx.doi.org/10.7840/kics.2011.36c.4.232.

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24

Larsson, E. G., and Yang Cao. "Collaborative transmit diversity with adaptive radio resource and power allocation." IEEE Communications Letters 9, no. 6 (June 2005): 511–13. http://dx.doi.org/10.1109/lcomm.2005.1437354.

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25

Xiao Juan Zhang and Yi Gong. "Adaptive Power Allocation for Multihop Regenerative Relaying With Limited Feedback." IEEE Transactions on Vehicular Technology 58, no. 7 (September 2009): 3862–67. http://dx.doi.org/10.1109/tvt.2009.2016207.

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26

Li, Li, Honglin Hu, Xia Chen, and Hsiao-Hwa Chen. "Adaptive cooperative coding with power allocation in wireless relay networks." IEEE Transactions on Wireless Communications 8, no. 9 (September 2009): 4604–15. http://dx.doi.org/10.1109/twc.2009.080605.

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27

Olfat, M., F. R. Farrokhi, and K. J. Ray Liu. "Power allocation for OFDM using adaptive beamforming over wireless networks." IEEE Transactions on Communications 53, no. 3 (March 2005): 505–14. http://dx.doi.org/10.1109/tcomm.2005.843438.

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28

Lee, Deokhui, and Jaewoo So. "Adaptive feedback bits and power allocation for dynamic TDD systems." Journal of Communications and Networks 21, no. 2 (April 2019): 113–24. http://dx.doi.org/10.1109/jcn.2019.000009.

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29

Maity, Santi P., Sumanta Hati, and Chinmay Maji. "Optimal power allocation in DS-CDMA with adaptive SIC technique." Telecommunication Systems 56, no. 3 (August 30, 2013): 335–46. http://dx.doi.org/10.1007/s11235-013-9847-2.

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30

Alhamad, Raed, and Hatem Boujemâa. "Correction to: Optimal power allocation for CRN-NOMA systems with adaptive transmit power." Signal, Image and Video Processing 14, no. 8 (May 13, 2020): 1717. http://dx.doi.org/10.1007/s11760-020-01704-5.

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31

Salh, Adeeb, Lukman Audah, Nor Shahida M Shah, and Shipun A. Hamzah. "Adaptive Antenna Selection and Power Allocation in Downlink Massive MIMO Systems." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 6 (December 1, 2017): 3521. http://dx.doi.org/10.11591/ijece.v7i6.pp3521-3528.

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Massive multi-input, multi-output (MIMO) systems are an exciting area of study and an important technique for fifth-generation (5G) wireless networks that support high data rate traffic. An increased number of antenna arrays at the base station (BS) consumes more power due to a higher number of radio frequency (RF) chains, which cannot be neglected and becomes a technical challenge. In this paper, we investigated how to obtain the maximal data rate by deriving the optimal number of RF chains from a large number of available antenna arrays at the BS when there is equal power allocation among users. Meanwhile, to mitigate inter-user-interference and to compute transmit power allocation, we used the precoding scheme zero forcing beamforming (ZFBF). The achievable data rate is increased because the algorithm of ZFBF enables the choosing of the maximum power in relation to the optimal antenna selection. We conclude that the transmit power allocation allows the use of less number of RF chains which provides the maximum achievable data rate depending on the optimal RF chain at the BS.

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32

Wei, Wei, and Wen Dong An. "Adaptive Bit Allocation Algorithm for OFDM System." Advanced Materials Research 989-994 (July 2014): 2161–64. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2161.

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For the greedy algorithm required a large number of iterations, this paper proposed one adaptive bit allocation algorithm based on Hughes-Hartogs algorithm which was improved greedy algorithm .Under the constraint of bit error rate and data transmission rate, the algorithm first used the Chow algorithm to allocate some of the bits, and then used the greedy algorithm to allocate the remaining bits. When minimize the total power by this algorithm, the iterations of this algorithm were significantly less than the greedy algorithm. By computer simulation, the results showed that performance of this algorithm was very close to the greedy algorithm, and the number of this algorithm’s iterations was 7.4%~34% of the greedy algorithm.

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33

Pao, Wei-Chen, and Yung-Fang Chen. "Adaptive Gradient-Based Methods for Adaptive Power Allocation in OFDM-Based Cognitive Radio Networks." IEEE Transactions on Vehicular Technology 63, no. 2 (February 2014): 836–48. http://dx.doi.org/10.1109/tvt.2013.2272804.

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34

Li, Su Fang, Ming Yan Jiang, An Ming Dong, and Dong Feng Yuan. "Adaptive Subcarrier, Bit and Power Allocation Based on Hopfield Neural Network for Multiuser OFDM." Applied Mechanics and Materials 325-326 (June 2013): 1706–11. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1706.

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A kind of adaptive subcarrier, bit and power allocation method utilizing Hopfield neural network (HNN) to minimize the overall transmit power of multiuser OFDM is studied in this paper. In order to find the power optimal subcarrier, bit and power allocation under the constraints that one subcarrier can only be allocated to one user and all users are allocated the same numbers of subcarrier, the number of bits of each subcarrier is finite, bit data can be allocated to each subcarrier, two kinds of new energy constrained functions are constructed for the HNN. It is shown through numerical simulations that the proposed methods can find the optimal allocation with less complexity compared with the exhaustive method.

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35

Viele, Kert, Kristine Broglio, Anna McGlothlin, and Benjamin R. Saville. "Comparison of methods for control allocation in multiple arm studies using response adaptive randomization." Clinical Trials 17, no. 1 (October 19, 2019): 52–60. http://dx.doi.org/10.1177/1740774519877836.

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Background/Aims: Response adaptive randomization has many polarizing properties in two-arm settings comparing control to a single treatment. The generalization of these features to the multiple arm setting has been less explored, and existing comparisons in the literature reach disparate conclusions. We investigate several generalizations of two-arm response adaptive randomization methods relating to control allocation in multiple arm trials, exploring how critiques of response adaptive randomization generalize to the multiple arm setting. Methods: We perform a simulation study to investigate multiple control allocation schemes within response adaptive randomization, comparing the designs on metrics such as power, arm selection, mean square error, and the treatment of patients within the trial. Results: The results indicate that the generalization of two-arm response adaptive randomization concerns is variable and depends on the form of control allocation employed. The concerns are amplified when control allocation may be reduced over the course of the trial but are mitigated in the methods considered when control allocation is maintained or increased during the trial. In our chosen example, we find minimal advantage to increasing, as opposed to maintaining, control allocation; however, this result reflects an extremely limited exploration of methods for increasing control allocation. Conclusion: Selection of control allocation in multiple arm response adaptive randomization has a large effect on the performance of the design. Some disparate comparisons of response adaptive randomization to alternative paradigms may be partially explained by these results. In future comparisons, control allocation for multiple arm response adaptive randomization should be chosen to keep in mind the appropriate match between control allocation in response adaptive randomization and the metric or metrics of interest.

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36

Yang, Xiao Long, Lin Ma, and Xue Zhi Tan. "Power-Weight Allocation Algorithm for OFDM-Based Cognitive Radio System." Advanced Materials Research 546-547 (July 2012): 932–36. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.932.

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Cognitive Radio (CR) has been proposed as one of the most promising technologies to provide efficient utilization of the limited wireless spectrum. In this paper, we investigate the power allocation for CR system based on orthogonal frequency division multiplexing (OFDM), and an improved allocation algorithm called power-weight algorithm is proposed. This algorithm performs initial allocation via Lagrange multiplier method, and then accomplishes second allocation based on power weight obtained in initial allocation. The analytical results reveal that the total transmission rate of all subcarriers is much closer to the optimal algorithm (i.e. the greedy algorithm) than the water-level algorithm, the Krongold algorithm and adaptive water-filling algorithm, subject to transmit power constraint and bit error rate (BER) constraint. These algorithms are all based on water-filling thought. Finally, simulation results verify our analysis.

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37

Gao, He, Jun Li, Fu Qiang Zhou, Rong Zou, and Yi Cui. "Outage Probability Analysis and Adaptive Power Allocation for ISDF MIMO Cooperation Systems." Applied Mechanics and Materials 58-60 (June 2011): 2296–302. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.2296.

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The outage probability performance is analyzed for the optimization of incremental selection decode-amplify-forward (ISDF) multi-node MIMO cooperative communication systems. Firstly, the system model for the proposed multi-node MIMO cooperative protocol. Then, the incremental selection decode-amplify-forward and optimal relay selection strategy (routing) based on the opportunistic relaying scheme is proposed. The mutual information and outage probability between source and destination nodes for the proposed scheme are formulated with the relays have the capability of maximum ratio combining (MRC) receiving and beam-forming transmitting capabilities. Finally, adaptive strategy is proposed for adaptive optimal power allocation (APA). Theoretical analysis and simulation results show that the proposed APA outperforms traditional equal power allocation (EPA) in outage performance.

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38

Wen, Juan, and Qi Ming Tian. "A Fast Adaptive Transmit Power and Bit Allocation in OFDM System." Advanced Materials Research 765-767 (September 2013): 444–47. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.444.

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In this paper, we propose a fast and optimal adaptive transmit power and bit allocation algorithm, which is called Bisection Searching Level (BSL) for wireless OFDMA systems. The algorithm aims at maximization of data rate under the constraints of total transmit power and bit error rate (BER). BSL is based on water pouring approach, and adopt bisection algorithm to find the water pouring level under discrete bit restriction. Both theoretical analysis and simulation results demonstrate the optimality of our proposed algorithm.

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39

Nadkar, Taskeen, Vinay Thumar, Tej G.P.S, Merchant S.N, and Desai U.B. "ADAPTIVE GUARD INTERVAL AND POWER ALLOCATION FOR OFDM-BASED COGNITIVE RADIO." ICTACT Journal on Communication Technology 02, no. 02 (June 1, 2011): 314–22. http://dx.doi.org/10.21917/ijct.2011.0044.

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40

Lin, Fei, Tao Jiang, and Tao Luo. "A correction in "distributed adaptive power allocation for wireless relay networks"." IEEE Transactions on Wireless Communications 8, no. 7 (July 2009): 3462–63. http://dx.doi.org/10.1109/twc.2009.081673.

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41

Moon, Hichan. "Optimum Power Allocation for Preamble Detection With Channel-Adaptive Random Access." IEEE Transactions on Wireless Communications 12, no. 11 (November 2013): 5424–33. http://dx.doi.org/10.1109/twc.2013.101613.120750.

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42

Paul Sam, Reba, and Uma Maheswari Govindaswamy. "Antenna selection and adaptive power allocation for IA‐based underlay CR." IET Signal Processing 11, no. 6 (August 2017): 734–42. http://dx.doi.org/10.1049/iet-spr.2016.0495.

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43

Huang, Xuan, Fang Yang, Changyong Pan, and Jian Song. "Advanced ADO-OFDM With Adaptive Subcarrier Assignment and Optimized Power Allocation." IEEE Wireless Communications Letters 8, no. 4 (August 2019): 1167–70. http://dx.doi.org/10.1109/lwc.2019.2910250.

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44

Yan, JiLei, JianDong Li, and LinJing Zhao. "Adaptive joint bandwidth and power allocation in heterogeneous wireless access environment." Science China Information Sciences 57, no. 6 (May 13, 2014): 1–14. http://dx.doi.org/10.1007/s11432-013-4953-z.

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Borui, YANG, ZHAO Li, LU Ying, and ZHOU Yu. "Research on MIMO channel capacity for adaptive power allocation visible light." Journal of Applied Optics 41, no. 3 (2020): 626–30. http://dx.doi.org/10.5768/jao202041.0308002.

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Heo, Joo, Insuk Cha, and KyungHi Chang. "Effective adaptive transmit power allocation algorithm considering dynamic channel allocation in reuse partitioning-based OFDMA system." Wireless Personal Communications 43, no. 2 (April 6, 2007): 677–84. http://dx.doi.org/10.1007/s11277-007-9272-5.

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Liu, Hui, Xunming Li, Weida Wang, Lijin Han, Huibin Xin, and Changle Xiang. "Adaptive equivalent consumption minimisation strategy and dynamic control allocation-based optimal power management strategy for four-wheel drive hybrid electric vehicles." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 233, no. 12 (December 12, 2018): 3125–46. http://dx.doi.org/10.1177/0954407018816564.

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An adaptive equivalent consumption minimisation strategy and dynamic control allocation-based optimal power management strategy for a four-wheel drive plug-in hybrid electric vehicle is proposed in this paper. The equivalent factors of adaptive equivalent consumption minimisation strategy are optimised offline based on ISIGHT software over several typical driving cycles, which is integrated with AVL CRUISE and MATLAB/Simulink. To update the equivalent factor adaptively according to the predictive velocity, a neural network-based optimal equivalent factor prediction model is built, which can be used online. The torque distribution strategy considering axle load based on energy management strategy optimisation results and the vehicle dynamics control distribution is proposed: this includes two-wheel drive torque distribution, four-wheel drive torque distribution and brake torque distribution. The proposed energy management strategy is verified in New European Driving Cycle and Worldwide harmonised Light Vehicle Test Cycle driving patterns, and the simulation results show that the fuel economy of adaptive equivalent consumption minimisation strategy and dynamic control allocation-based optimal power management strategy is improved by 8.84% and 7.52% in New European Driving Cycle and Worldwide harmonised Light Vehicle Test Cycle, respectively, compared with the benchmark algorithm-based strategy.

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Shareef, Hussain, Saifulnizam Abd Khalid, Mohd Wazir Mustafa, and Azhar Khairuddin. "An ANFIS Approach for Real Power Transfer Allocation." Journal of Applied Mathematics 2011 (2011): 1–14. http://dx.doi.org/10.1155/2011/414258.

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This paper proposes an adaptive neurofuzzy interface system (ANFIS) approach to identify the real power transfer between generators. Based on solved load flow results, it first uses modified nodal equation method (MNE) to determine real power contribution from each generator to loads. Then the results of MNE method and load flow information are utilized to train the designed ANFIS. It also incorporated an enhanced feature extraction method called principle component analysis (PCA) to reduce the input features to the ANFIS. The 25-bus equivalent system of south Malaysia is utilized as a test system to illustrate the effectiveness of the ANFIS output compared to that of the MNE method. The ANFIS output provides promising results in terms of accuracy and computation time. Furthermore, it can be concluded that the ANFIS with enhanced feature extraction method reduces the time taken to train the ANFIS without affecting the accuracy of the results.

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Sun, Xiaobo, Weirong Liu, Mengfei Wen, Yue Wu, Heng Li, Jiahao Huang, Chao Hu, and Zhiwu Huang. "A Real-Time Optimal Car-Following Power Management Strategy for Hybrid Electric Vehicles with ACC Systems." Energies 14, no. 12 (June 10, 2021): 3438. http://dx.doi.org/10.3390/en14123438.

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This paper develops a model predictive multi-objective control framework based on an adaptive cruise control (ACC) system to solve the energy allocation and battery state of charge (SOC) maintenance problems of hybrid electric vehicles in the car-following scenario. The proposed control framework is composed of a car-following layer and an energy allocation layer. In the car-following layer, a multi-objective problem is solved to maintain safety and comfort, and the generated speed sequence in the prediction time domain is put forward to the energy allocation layer. In the energy allocation layer, an adaptive equivalent-factor-based consumption minimization strategy with the predicted velocity sequences is adopted to improve the engine efficiency and fuel economy. The equivalent factor reflects the extent of SOC variation, which is used to maintain the battery SOC level when optimizing the energy. The proposed controller is evaluated in the New York City Cycle (NYCC) driving cycle and the Urban Dynamometer Driving Schedule (UDDS) driving cycle, and the comparison results demonstrate the effectiveness of the proposed controller.

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Lin, Jia-Chin, and Chih-Yen Liu. "Power Allocation Adaptation With Coherence Time for Nonorthogonal Multiple Access on Downlink Transmission." International Journal of Interdisciplinary Telecommunications and Networking 13, no. 3 (July 2021): 54–71. http://dx.doi.org/10.4018/ijitn.2021070104.

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Nonorthogonal multiple access (NOMA) methods are expected to improve spectral efficiency or increase sum rate in a recent mobile communication network. The performance of the power-domain NOMA highly relies on the effectiveness of successive interference cancelation (SIC) and power allocation techniques. This paper proposes an adaptive power allocation method according to analytical coherence time of time-varying channels to improve the SIC effectiveness. Simulations show that the proposed technique can improve SIC, therefore reducing error probability without reduction of the sum rate.

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