scholarly journals Power Allocation in Massive MIMO-HWSN Based on the Water-Filling Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhe Li ◽  
Sahil Verma ◽  
Machao Jin
Keyword(s):  
Power Allocation ◽  
System Capacity ◽  
Power Method ◽  
Transmission Power ◽  
Uplink Capacity ◽  
Heterogeneous Wireless Sensor Networks ◽  
Negative Effect ◽  
Water Filling ◽  
Water Filling Algorithm ◽  
Iot Devices

Pilot power allocation for Internet of Things (IoT) devices in massive multi-input multioutput heterogeneous wireless sensor networks (MIMO-HWSN) is studied in this paper. The interference caused by fractional pilot reusing in adjacent cells had a negative effect on the MIMO-HWSN system performance. Reasonable power allocation for users can effectively weaken the interference. Motivated by the water-filling algorithm, we proposed a suboptimal pilot transmission power method to improve the system capacity. Simulation results show that the proposed method can significantly improve the uplink capacity of the system and explain the influence of different pilot transmission power on the performance of the system, but the complexity of the system almost does not increase.

Capacity Analysis of OAM Multiplexing System Based on Various Detection Schemes

2016 ◽  
Vol 850 ◽  
pp. 105-111
Author(s):  
Li An Bian ◽  
Pei Guo Liu ◽  
Zhao Wen Zhuang ◽  
Gang Ou ◽  
Wei Dong Hu ◽  
...  
Keyword(s):  
Power Allocation ◽  
Electromagnetic Waves ◽  
Minimum Mean Square Error ◽  
System Capacity ◽  
The Novel ◽  
Zero Forcing ◽  
Radio Communications ◽  
Water Filling ◽  
Water Filling Algorithm ◽  
Detection Schemes

Orbital angular momentum of vortex electromagnetic waves has the potential to overwhelmingly increase the system capacity for radio communications. To explore the performance of the novel multiplexing system, the influence of both detection schemes at the receiving side and power allocation methods at the transmitting side on channel capacity is investigated firstly by exploiting zero-forcing (ZF) and minimum mean square error (MMSE) detection schemes and water-filling algorithm. It is found finally that MMSE is superior to ZF generally, which conforms to the MIMO theory. And for rank-deficient channel, optimal power allocation will play a greater role to enhance system capacity.

scholarly journals Improved Water-Filling Power Allocation for Energy-Efficient Massive MIMO Downlink Transmissions

2017 ◽  
Vol 63 (1) ◽  
pp. 79-84
Author(s):  
M. K Noor Shahida ◽  
Rosdiadee Nordin ◽  
Mahamod Ismail
Keyword(s):  
Power Allocation ◽  
Network Architecture ◽  
Massive Mimo ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Downlink Transmission ◽  
Allocation Algorithm ◽  
Mimo Antenna ◽  
Water Filling ◽  
Water Filling Algorithm

Abstract Energy Efficiency (EE) is becoming increasingly important for wireless communications and has caught more attention due to steadily rising energy costs and environmental concerns. Recently, a new network architecture known as Massive Multiple-Input Multiple-Output (MIMO) has been proposed with the remarkable potential to achieve huge gains in EE with simple linear processing. In this paper, a power allocation algorithm is proposed for EE to achieve the optimal EE in Massive MIMO. Based on the simplified expression, we develop a new algorithm to compute the optimal power allocation algorithm and it has been compared with the existing scheme from the previous literature. An improved water filling algorithm is proposed and embedded in the power allocation algorithm to maximize EE and Spectral Efficiency (SE). The numerical analysis of the simulation results indicates an improvement of 40% in EE and 50% in SE at the downlink transmission, compared to the other existing schemes. Furthermore, the results revealed that SE does not influence the EE enhancement after using the proposed algorithm as the number of Massive MIMO antenna at the Base Station (BS) increases.

scholarly journals Power allocation in OFDM-based cognitive radio systems using iterative algorithms

2021 ◽  
Author(s):  
Aniqua Tasnim Rahman Antora
Keyword(s):  
Cognitive Radio ◽  
Power Allocation ◽  
Peak Power ◽  
Radio Resource ◽  
Power Constraint ◽  
Power Constraints ◽  
Cognitive Radio Systems ◽  
Water Filling ◽  
Radio Systems ◽  
Water Filling Algorithm

As spectrum scarcity is becoming a serious problem, the worth of finding a general solution for such issue has become even serious due to the rapid development of wireless communications. The main objective of this thesis is to investigate the optimal power allocation procedure that maximizes the capacity in OFDM based Cognitive Radio Systems. The main purpose of the search is to modify the conventional water-filling algorithm applied in general OFDM based Cognitive Radio systems due to the per subchannel power constraints and individual peak power constraints. For Radio Resource Allocation (RRA), one of the most typical problems is to solve power allocation using the Conventional Water- filling. As communication system develops, the structures of the system models and the corresponding RRA problems evolve to more advanced and more complicated ones. In this thesis Iterative Partitioned Weighted Geometric Water-filling with Individual Peak Power Constraints (IGPP), a simple and elegant approach is proposed to solve the weighted radio resource allocation problem with peak power constraint and total subchannel power constraint with channel partitions. The proposed IGPP algorithm requires less computation than the Conventional Water-filling algorithm (CWF). Dynamic Channel Sensing Iterative (DCSI) approach is another algorithm proposed to optimally allocate power for OFDM based Cognitive Radio Systems. DCSI is a innovative concept which will allow us to solve the same problem intelligently with less complexity. It provides straight forward power allocation analysis, solutions and insights with reduced computation over other approaches under the same memory requirement and sorted parameters.

scholarly journals Power allocation in OFDM-based cognitive radio systems

2021 ◽  
Author(s):  
Shibiao Zhao
Keyword(s):  
Cognitive Radio ◽  
Power Allocation ◽  
Optimal Power Allocation ◽  
Power Constraint ◽  
Interference Constraints ◽  
Allocation Algorithm ◽  
Optimal Power ◽  
Water Filling ◽  
Water Filling Algorithm ◽  
Allocation Algorithms

In this thesis, we develop an subcarrier transmission suboptimal power allocation algorithm and an underlay subcarrier transmission optimal power allocation algorithm for the orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems with different statistical interference constraints imposed by different primary users (PUs). Given the fact that the interference constraints are met in a statistical manner, the CR transmitter does not require the instantaneous channel quality feed-back from the PU receivers. First an alternative subcarrier transmission suboptimal algorithm with reduced complexity has been proposed and the performance has been investigated. Presented numerical results show that with our proposed suboptimal power allocation algorithm CR user can achieve 10 percent higher transmission capacity for given statistical interference constraints and a given power budget compared to the traditional suboptimal power allocation algorithms, uniform and water-filling power allocation algorithms. The proposed suboptimal algorithm outperforms traditional suboptimal algorithm, water-filling algorithm and uniform power loading algorithm. Second,We introduce an underlay subcarrier transmission optimal power allocation algorithms which allows the secondary users use the bandwidth used by Pus. And at the same time we consider the individual peak power constraint as the forth constraint added to the objective function which is the transmission capacity rate of the secondary users.Third, we propose suboptimal algorithm using GWF which has less complexity level than traditional water-filling algorithm instead of conventional water-filling algorithm in calculating the assigned power while considering the satisfaction of the total power constraint. The proposed suboptimal algorithm gives an option of using a low complexity power allocation algorithm where complexity is an issue.

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