Relevant bibliographies by topics / Adaptive transmit power allocation

Academic literature on the topic 'Adaptive transmit power allocation'

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Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Adaptive transmit power allocation"

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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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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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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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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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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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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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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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Li, Suoping, Wenwu Liang, Vicent Pla, Nana Yang, and Sa Yang. "Two-Stage Adaptive Relay Selection and Power Allocation Strategy for Cooperative CR-NOMA Networks in Underlay Spectrum Sharing." Applied Sciences 11, no. 21 (November 6, 2021): 10433. http://dx.doi.org/10.3390/app112110433.

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In this paper, we consider a novel cooperative underlay cognitive radio network based on non-orthogonal multiple access (CR-NOMA) with adaptive relay selection and power allocation. In secondary networks, dedicated relay assistance and user assistance are used to achieve communication between the base station and the far (and near) user. Here, a two-stage adaptive relay selection and power allocation strategy is proposed to maximize the achievable data rate of the far user while ensuring the service quality of near user. Furthermore, the closed-form expressions of outage probability of two secondary users are derived, respectively, under interference power constraints, revealing the impact of transmit power, number of relays, interference threshold and target data rate on system outage probability. Numerical results and simulations validate the advantages of the established cooperation and show that the proposed adaptive relay selection and power allocation strategy has better outage performance.
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Guo, Xinyue, Keer Zhang, and Xufa Huang. "Switching MIMO System with Adaptive OFDM Modulation for Indoor Visible Light Communication." Advances in Condensed Matter Physics 2018 (September 2, 2018): 1–7. http://dx.doi.org/10.1155/2018/5694196.

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In this paper, we propose and experimentally demonstrate a switching multiple input and multiple output (MIMO) system combining with adaptive orthogonal frequency division multiplexing (OFDM) modulation for high-speed indoor visible light communications. The adaptive OFDM modulation, which is realized by power and bit allocation on OFDM subchannels, is utilized to achieve the maximum channel capacity under a given target bit error rate (BER). Meanwhile, the MIMO mode switches between spatial multiplexing and transmit diversity adapting to the channel correlation, where the modulation order solved by adaptive OFDM modulation is chosen as the switching criterion. Experimental results validate data rates improvement over the pure spatial multiplexing and the pure transmit diversity system, where BERs are all below the 7% preforward error correction (pre-FEC) threshold of 3.8 × 10−3 in experiments.
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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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Dissertations / Theses on the topic "Adaptive transmit power allocation"

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Tabatabaei, Yazdi Ehsan. "Adaptive Resource Allocation for Wireless Body Sensor Networks." Thesis, University of Canterbury. Computer Science and Software Engineering, 2014. http://hdl.handle.net/10092/9828.

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The IEEE 802.15.4 standard is an interesting technology for use in Wireless Body Sensor Networks (WBSN), where entire networks of sensors are carried by humans. In many environments the sensor nodes experience external interference for example, when the WBSN is operated in the 2.4 GHz ISM band and the human moves in a densely populated city, it will likely experience WiFi interference, with a quickly changing ``interference landscape''. In this thesis we propose Adaptive Resource Allocation schemes, to be carried out by the WBSN, which provided noticeable performance gains in such environments. We investigate a range of adaptation schemes and assess their performance both through simulations and experimentally.
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Karim, Md Anisul. "Weighted layered space-time code with iterative detection and decoding." Thesis, The University of Sydney, 2006. http://hdl.handle.net/2123/1095.

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Multiple antenna systems are an appealing candidate for emerging fourth-generation wireless networks due to its potential to exploit space diversity for increasing conveyed throughput without wasting bandwidth and power resources. Particularly, layered space-time architecture (LST) proposed by Foschini, is a technique to achieve a significant fraction of the theoretical capacity with a reasonable implementation complexity. There has been a great deal of challenges in the detection of space-time signal; especially to design a low-complexity detector, which can efficiently remove multi-layer interference and approach the interference free bound. The application of iterative principle to joint detection and decoding has been a promising approach. It has been shown that, the iterative receiver with parallel interference canceller (PIC) has a low linear complexity and near interference free performance. Furthermore, it is widely accepted that the performance of digital communication systems can be considerably improved once the channel state information (CSI) is used to optimize the transmit signal. In this thesis, the problem of the design of a power allocation strategy in LST architecture to simultaneously optimize coding, diversity and weighting gains is addressed. A more practical scenario is also considered by assuming imperfect CSI at the receiver. The effect of channel estimation errors in LST architecture with an iterative PIC receiver is investigated. It is shown that imperfect channel estimation at an LST receiver results in erroneous decision statistics at the very first iteration and this error propagates to the subsequent iterations, which ultimately leads to severe degradation of the overall performance. We design a transmit power allocation policy to take into account the imperfection in the channel estimation process. The transmit power of various layers is optimized through minimization of the average bit error rate (BER) of the LST architecture with a low complexity iterative PIC detector. At the receiver, the PIC detector performs both interference regeneration and cancellation simultaneously for all layers. A convolutional code is used as the constituent code. The iterative decoding principle is applied to pass the a posteriori probability estimates between the detector and decoders. The decoder is based on the maximum a posteriori (MAP) algorithms. A closed-form optimal solution for power allocation in terms of the minimum BER is obtained. In order to validate the effectiveness of the proposed schemes, substantial simulation results are provided.
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Karim, Md Anisul. "Weighted layered space-time code with iterative detection and decoding." School of Electrical & Information Engineering, 2006. http://hdl.handle.net/2123/1095.

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Master of Engineering (Research)
Multiple antenna systems are an appealing candidate for emerging fourth-generation wireless networks due to its potential to exploit space diversity for increasing conveyed throughput without wasting bandwidth and power resources. Particularly, layered space-time architecture (LST) proposed by Foschini, is a technique to achieve a significant fraction of the theoretical capacity with a reasonable implementation complexity. There has been a great deal of challenges in the detection of space-time signal; especially to design a low-complexity detector, which can efficiently remove multi-layer interference and approach the interference free bound. The application of iterative principle to joint detection and decoding has been a promising approach. It has been shown that, the iterative receiver with parallel interference canceller (PIC) has a low linear complexity and near interference free performance. Furthermore, it is widely accepted that the performance of digital communication systems can be considerably improved once the channel state information (CSI) is used to optimize the transmit signal. In this thesis, the problem of the design of a power allocation strategy in LST architecture to simultaneously optimize coding, diversity and weighting gains is addressed. A more practical scenario is also considered by assuming imperfect CSI at the receiver. The effect of channel estimation errors in LST architecture with an iterative PIC receiver is investigated. It is shown that imperfect channel estimation at an LST receiver results in erroneous decision statistics at the very first iteration and this error propagates to the subsequent iterations, which ultimately leads to severe degradation of the overall performance. We design a transmit power allocation policy to take into account the imperfection in the channel estimation process. The transmit power of various layers is optimized through minimization of the average bit error rate (BER) of the LST architecture with a low complexity iterative PIC detector. At the receiver, the PIC detector performs both interference regeneration and cancellation simultaneously for all layers. A convolutional code is used as the constituent code. The iterative decoding principle is applied to pass the a posteriori probability estimates between the detector and decoders. The decoder is based on the maximum a posteriori (MAP) algorithms. A closed-form optimal solution for power allocation in terms of the minimum BER is obtained. In order to validate the effectiveness of the proposed schemes, substantial simulation results are provided.
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Shi, Zhengyan 1975. "Transmit antenna selected spatial multiplexing systems with power allocation." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=99538.

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Vertical Bell Lab Layered Space-Time (VBLAST) is an emerging spatial modulation technique, that provides high spectral efficiency due to its use of multiple transmit and receiver antennas. We present novel schemes; consisting of combinations of Transmit Antenna Selection (TAS) with Power Allocation (PA), to improve the error rate performance of VBLAST systems. Our schemes provide significant performance gain over VLAST because of enhanced transmit diversity introduced by transmit antenna selection. Our scheme may be used for uplink wireless system, where the total power is limited by the mobile terminal battery. The effect of limited rate feedback link is investigated by sending quantized power weights from receiver to transmitter. The simulation results show that even one-bit, codebook for power weights can achieve performance close to that of unlimited rate feedback link in low and medium SNR regions. We also present a novel PA technique alloting power in the joint space-frequency domain to reduce the error rate, and apply our scheme to frequency selective MIMO-OFDM channels. In addition, an analytical analysis of transmit correlated Successive interference Cancelling Zero Forcing (SIC-ZF) VBLAST in flat fading channels is presented. Over frequency selective channels, the performance of SIC-ZF VBLAST is assessed by computer simulation.
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Chung, Jong-Sun. "Fast Power Allocation Algorithms for Adaptive MIMO Systems." Thesis, University of Canterbury. Electrical and Computer Engineering, 2009. http://hdl.handle.net/10092/3764.

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Recent research results have shown that the MIMO wireless communication architecture is a promising approach to achieve high bandwidth efficiencies. MIMO wireless channels can be simply defined as a link for which both the transmitting and receiving ends are equipped with multiple antenna elements. Adaptive modulation and power allocation could be used to further improve the performance of MIMO systems. This thesis focuses on developing a fast and high performance power allocation algorithm. Three power allocation algorithms are proposed in this thesis and their performances are compared in various system sizes and transceiver architectures. Among the three algorithms proposed in this thesis, the fast algorithm may be considered as the best power allocation algorithm since the performance of the fast algorithm is almost as good as the fullsearch (optimal)algorithm and the mean processing time is considerably less than the fullsearch algorithm. The fast algorithm achieves about 97.6% agreement with the optimal throughput on average. In addition, the time taken to find the power scaling factors using the fullsearch algorithm is about 2300 times longer than the processing time of the fast algorithm in a 6 x 6 system when the SNR is 20dB. As an extension to the power allocation process, excess power allocation methods are introduced. Excess power is the unused power during the power allocation process. The power allocation algorithm allocates power to each received SNR to maximize the throughput of the system whereas the excesspower allocation distributes the excess power to each SNR to improve both the instantaneous and temporal behavior of the system. Five different excess power allocation methods are proposed in this thesis. These methods were simulated in the Rayleigh fading channel with different Doppler frequencies, fD = 10Hz,50Hz and 100Hz, where the ACF of the channel coefficients are given by the Jakes' model. The equal BER improvement method showed a slightly better performance than the other methods. The equal BER improvement method enables the system to maintain the power scaling factors without sacrificing QoS for 19.6 ms on average when the maximum Doppler shift is 10Hz.
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Berggren, Fredrik. "Power control and adaptive resource allocation in DS-CDMA systems." Doctoral thesis, KTH, Signals, Sensors and Systems, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3568.

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Ahmed, Safayet N. "Adaptive CPU-budget allocation for soft-real-time applications." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/52215.

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The focus of this dissertation is adaptive CPU-budget allocation for periodic soft-real-time applications. The presented algorithms are developed in the context of a power-management framework. First, the prediction-based bandwidth scheduler (PBS) is developed. This algorithm is designed to adapt CPU-budget allocations at a faster rate than previous adaptive algorithms. Simulation results are presented to demonstrate that this approach allows for a faster response to under allocations than previous algorithms. A second algorithm is presented called Two-Stage Prediction (TSP) that improves on the PBS algorithm. Specifically, a more sophisticated algorithm is used to predict execution times and a stronger guarantee is provided on the timeliness of jobs. Implementation details and experimental results are presented for both the PBS and TSP algorithms. An abstraction is presented called virtual instruction count (VIC) to allow for more efficient budget allocation in power-managed systems. Power management decisions affect job-execution times. VIC is an abstract measure of computation that allows budget allocations to be made independent of power-management decisions. Implementation details and experimental results are presented for a VIC-based budget mechanism. Finally, a power-management framework is presented called the linear adaptive models based system (LAMbS). LAMbS is designed to minimize power consumption while honoring budget allocations specified in terms of VIC.
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Cardieri, Paulo. "Resource Allocation and Adaptive Antennas in Cellular Communications." Diss., Virginia Tech, 2000. http://hdl.handle.net/10919/29051.

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The rapid growth in demand for cellular mobile communications and emerging fixed wireless access has created the need to increase system capacity through more efficient utilization of the frequency spectrum, and the need for better grade of service. In cellular systems, capacity improvement can be achieved by reducing co-channel interference. Several techniques have been proposed in literature for mitigating co-channel interference, such as adaptive antennas and power control. Also, by allocating transmitter power and communication channels efficiently (resource allocation), overall co-channel interference can be maintained below a desired maximum tolerable level, while maximizing the carried traffic of the system. This dissertation presents investigation results on the performance of base station adaptive antennas, power control and channel allocation, as techniques for capacity improvement. Several approaches are analyzed. Firstly, we study the combined use of adaptive antennas and fractional loading factor, in order to estimate the potential capacity improvement achieved by adaptive antennas. Next, an extensive simulation analysis of a cellular network is carried out aiming to investigate the complex interrelationship between power control, channel allocation and adaptive antennas. In the first part of this simulation analysis, the combined use of adaptive antennas, power control and reduced cluster size is analyzed in a cellular system using fixed channel allocation. In the second part, we analyze the benefits of combining adaptive antennas, dynamic channel allocation and power control. Two representative channel allocation algorithms are considered and analyzed regarding how efficiently they transform reduced co-channel interference into higher carried traffic. Finally, the spatial filtering capability of adaptive antennas is used to allow several users to share the same channel within the same cell. Several allocation algorithms combined with power control are analyzed.
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Liu, Feng. "Lifetime maximization through adaptive power allocation in reconfigurable system design for wireless systems /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?ECED%202009%20LIU.

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Alam, Muhammad Mahtab. "Power-Aware adaptive techniques for wireless sensor networks." Thesis, Rennes 1, 2013. http://www.theses.fr/2013REN1S049/document.

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Les Réseaux de capteurs sans fil (WSN) sont une technologie émergente avec des applications potentielles dans divers domaines de la vie quotidienne, tels que la surveillance structurelle et environnementale, la médecine, la surveillance militaire, les explorations robotisées, etc. Les nœuds de capteurs doivent fonctionner pendant une longue période avec des batteries capacité limitée, par conséquent le facteur plus important dans les WSN est la consommation d'énergie. Dans cette thèse, nous proposons des techniques d'optimisation algorithmiques dynamiques, et adaptative pour la réduction de l'énergie. Tout d'abord, un modèle énergétique précis est présenté. Ce modèle repose sur des mesures réelles de courant consommé pour différents scénarios qui peuvent se produire lors de la communication entre les nœud. Il en est conclu que la couche MAC joue un rôle essentiel dans la réduction de l'énergie consommée. Ensuite, un protocole MAC dynamique est présenté. Il adapte de manière dynamique l’intervalle de réveil des nœuds de capteurs à partir d’une estimation du trafic. L’algorithme adaptatif modélisé de façon heuristique pour comprendre le comportement de convergence des paramètres algorithmiques. Le protocole est appliqué sur des réseaux de capteurs corporels et il surclasse les autres protocoles MAC en termes de latence ainsi que de consommation d'énergie ce qui permet donc d'augmenter la durée de vie de trois à six fois. Enfin, une technique basée sur l’optimisation adaptative de la puissance d'émission radio est appliquée sur des canaux variant dans le temps. La puissance de sortie est réglée dynamiquement au meilleur niveau de puissance selon l’état du canal, ce qui diminue la consommation d’un facteur deux
Wireless Sensor Networks (WSN) are a fast emerging technology with potential applications in various domains of daily-life, such as structural and environmental monitoring, medicine, military surveillance, robotic explorations etc. WSN devices are required to operate for a long time with limited battery capacity, therefore, the most important constraint in WSN is energy consumption. In this thesis, we propose algorithmic-level dynamic and adaptive optimization techniques for energy reduction in WSN. First, an accurate energy model is presented. This model relies on real-time power measurements of various scenarios that can occur during communication between sensor nodes. It is concluded that MAC layer plays a pivotal role for energy reduction. Then, a traffic-aware dynamic MAC protocol is presented which dynamically adapts the wake-up schedule of sensor nodes through traffic estimation. An adaptive algorithm is designed for this purpose that is heuristically modeled to understand the convergence behavior of algorithmic parameters. The proposed protocol is applied to body area networks and it outperforms other low-power MAC protocols in terms of latency as well as energy consumption and consequently increases the lifetime from three to six times. Finally, an SNR-based adaptive transmit power optimization technique is applied under time-varying channels. The output power is dynamically tuned to best power level under slow varying channel, which results in an average gain by two times
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Book chapters on the topic "Adaptive transmit power allocation"

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You, Xiaohu, Dongming Wang, and Jiangzhou Wang. "Transmit Power Allocation and Energy Efficiency Optimization of Distributed MIMO." In Distributed MIMO and Cell-Free Mobile Communication, 89–107. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-9845-6_5.

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Liu, Zuoliang, and Shanxue Chen. "Joint Transmit Power Allocation and Power Splitting for SWIPT System with Time Reversal." In Simulation Tools and Techniques, 427–36. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32216-8_41.

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Zhang, Jingmei, Ying Wang, and Ping Zhang. "STC-Based Cooperative Relaying System with Adaptive Power Allocation." In Lecture Notes in Computer Science, 343–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11424505_33.

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Du, Tianyu, Zhipeng Wang, Dimitrios Makrakis, and Hussein T. Mouftah. "Adaptive Transmit Power Adjustment Technique for ZigBee Network under Wi-Fi Interference." In Ad Hoc Networks, 146–57. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13329-4_13.

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Lee, Ye Hoon. "A Multicarrier CDMA Communication System with Adaptive Transmission Power Allocation." In Communications in Computer and Information Science, 404–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35264-5_54.

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Wang, Lei, Jun Lu, XianQing Ling, and Qian Huang. "Low Computation Resource Allocation for Adaptive OFDM Power Line Communication." In Advances in Intelligent and Soft Computing, 671–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30223-7_106.

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Lee, Ye Hoon. "A Multicarrier CDMA Communication System with Adaptive Transmission Power Allocation." In Frontiers in Computer Education, 769–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27552-4_101.

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Le Thi, Hoai An, Thi Thuy Tran, Tao Pham Dinh, and Alain Gély. "DC Programming and DCA for Transmit Beamforming and Power Allocation in Multicasting Relay Network." In Advanced Computational Methods for Knowledge Engineering, 29–41. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-38884-7_3.

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Wu, Xuan-li, Ming-xin Luo, Lu-kuan Sun, and Nan-nan Fu. "User Fairness-Based Adaptive Power Allocation in TD-LTE-A Downlink." In Advances in Intelligent Systems and Computing, 423–30. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1759-6_49.

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Maity, Santi P., and Sumanta Hati. "Adaptive Power Allocation in CI/MC-CDMA System Using Genetic Algorithms." In Advances in Computing and Communications, 580–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22720-2_61.

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Conference papers on the topic "Adaptive transmit power allocation"

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Gui, Ronghua, and Wen-Qin Wang. "Adaptive Transmit Power Allocation for FDA Radar With Spectral Interference Avoidance." In 2020 IEEE Radar Conference (RadarConf20). IEEE, 2020. http://dx.doi.org/10.1109/radarconf2043947.2020.9266663.

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Yang Tang, B. Vucetic, and Hayoung Yang. "An optimal and adaptive transmit power allocation scheme for mimo system." In IEEE International Symposium on Information Theory, 2003. Proceedings. IEEE, 2003. http://dx.doi.org/10.1109/isit.2003.1228287.

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Wen, Juan, and Qiming Tian. "A Fast adaptive transmit power and bit allocation in OFDM system." In 2nd International Conference On Systems Engineering and Modeling. Paris, France: Atlantis Press, 2013. http://dx.doi.org/10.2991/icsem.2013.243.

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D'Oro, Salvatore, Panayotis Mertikopoulos, Aris L. Moustakas, and Sergio Palazzo. "Adaptive transmit policies for cost-efficient power allocation in multi-carrier systems." In 2014 12th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt). IEEE, 2014. http://dx.doi.org/10.1109/wiopt.2014.6850271.

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Qiu Yong-hong and Pan Ya-Han. "Adaptive bit and power allocation with adaptive transmit diversity for broadband MISO/OFDM wireless transmission." In Proceedings of 2003 International Conference on Neural Networks and Signal Processing. IEEE, 2003. http://dx.doi.org/10.1109/icnnsp.2003.1281153.

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Chen, Xiao-min, Da-zhuan Xu, and Xiang-bin Yu. "Adaptive transmit power allocation scheme for V-BLAST system under imperfect channel state information." In 2008 8th International Symposium on Antennas, Propagation & EM Theory (ISAPE - 2008). IEEE, 2008. http://dx.doi.org/10.1109/isape.2008.4735509.

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Civil, Musa, and Ozgur Ertug. "A New Algorithm with Adaptive Power Allocation (APA) for Variable Transmit Antenna Selection under MISO-SCMA Systems." In 2019 International Conference on Software, Telecommunications and Computer Networks (SoftCOM). IEEE, 2019. http://dx.doi.org/10.23919/softcom.2019.8903781.

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Chen, Xiao-min, Da-zhuan Xu, Xiang-bin Yu, and Qiu-ming Zhu. "An Adaptive Transmit Antenna Selection and Power Allocation Scheme for V-BLAST System under Imperfect Channel State Information." In 2009 International Conference on Networks Security, Wireless Communications and Trusted Computing (NSWCTC). IEEE, 2009. http://dx.doi.org/10.1109/nswctc.2009.294.

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Lingyan Fan, Chen He, Zhiying Wang, and Xiaolin Che. "Transmit power and bit allocation for the MIMO system." In GLOBECOM '05. IEEE Global Telecommunications Conference, 2005. IEEE, 2005. http://dx.doi.org/10.1109/glocom.2005.1578285.

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Hayashi, K., T. Fujii, M. Kaneko, H. Sakai, and Y. Okada. "Transmit beamforming and power allocation for downlink OFDMA systems." In 2009 7th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks - WiOpt 2009. IEEE, 2009. http://dx.doi.org/10.1109/wiopt.2009.5291589.

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