Low complexity real-time power allocation algorithm to improve BER performance in cooperative communication systems

2009 ◽  
Author(s):  
Liang Wang ◽  
Hui-min Chen ◽  
Wei-hao Xie ◽  
Zhuo Wu
Keyword(s):  
Power Allocation ◽  
Real Time ◽  
Cooperative Communication ◽  
Communication Systems ◽  
Low Complexity ◽  
Allocation Algorithm ◽  
Ber Performance

scholarly journals Low-complexity beam-domain channel estimation and power allocation in hybrid architecture massive MIMO systems

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Xiao Chen ◽  
Zaichen Zhang ◽  
Liang Wu ◽  
Jian Dang
Keyword(s):  
Channel Estimation ◽  
Power Allocation ◽  
Spectral Efficiency ◽  
Communication Systems ◽  
Mimo Systems ◽  
Beam Steering ◽  
Estimation Method ◽  
Low Complexity ◽  
Estimation Accuracy ◽  
Hybrid Architecture

Abstract In this journal, we investigate the beam-domain channel estimation and power allocation in hybrid architecture massive multiple-input and multiple-output (MIMO) communication systems. First, we propose a low-complexity channel estimation method, which utilizes the beam steering vectors achieved from the direction-of-arrival (DOA) estimation and beam gains estimated by low-overhead pilots. Based on the estimated beam information, a purely analog precoding strategy is also designed. Then, the optimal power allocation among multiple beams is derived to maximize spectral efficiency. Finally, simulation results show that the proposed schemes can achieve high channel estimation accuracy and spectral efficiency.

scholarly journals Power Allocation Algorithm for an Energy-Harvesting Wireless Transmission System Considering Energy Losses

Algorithms ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 25
Author(s):  
Su Zhao ◽  
Gang Huang ◽  
Qi Zhu
Keyword(s):  
Energy Harvesting ◽  
Power Allocation ◽  
Online Algorithm ◽  
Optimization Methods ◽  
Low Complexity ◽  
Transmission System ◽  
Wireless Transmission ◽  
Energy Losses ◽  
Average Throughput ◽  
Allocation Algorithm

For an energy-harvesting wireless transmission system, considering that a transmitter which can harvest energy from nature has two kinds of extra energy consumption, circuit consumption and storage losses, the optimization models are set up in this paper for the purpose of maximizing the average throughput of the system within a certain period of time for both a time-invariant channel and time-varying channel. Convex optimization methods such as the Lagrange multiplier method and the KKT (Karush–Kuhn–Tucker) condition are used to solve the optimization problem; then, an optimal offline power allocation algorithm which has a three-threshold structure is proposed. In the three-threshold algorithm, two thresholds can be achieved by using a linear search method while the third threshold is calculated according to the channel state information and energy losses; then, the offline power allocation is based on the three thresholds and energy arrivals. Furthermore, inspired by the optimal offline algorithm, a low-complexity online algorithm with adaptive thresholds is derived. Finally, the simulation results show that the offline power allocation algorithms proposed in this paper are better than other algorithms, the performance of the online algorithm proposed is close to the offline one, and these algorithms can help improve the average throughput of the system.

scholarly journals PAPR Reduction in MIMO-OFDM Systems Using Low- Complexity Additive Signal Mixing

2021 ◽  
pp. 468-478
Author(s):  
Stephen Kiambi ◽  
◽  
Elijah Mwangi ◽  
George Kamucha
Keyword(s):  
Communication Systems ◽  
Window Size ◽  
Low Complexity ◽  
Data Rate ◽  
Papr Reduction ◽  
Ofdm Systems ◽  
High Data ◽  
Communication Technique ◽  
Mimo Ofdm ◽  
Ber Performance

A MIMO-OFDM wireless communication technique possesses several advantages accrued from combining MIMO and OFDM techniques such as increased channel capacity and improved BER performance. This has made the technique very amiable to current and future generations of communication systems for high data-rate transmission. However, the technique also inherits the high PAPR problem associated with OFDM signals—a problem still requiring a practical solution. This work proposes a PAPR reduction algorithm for solving the problem of high PAPR in MIMO-OFDM systems. The proposed method uses a low-complexity signal mixing concept to combine the original transmit signal and a generated peak-cancelling signal. The computational complexity of the proposed method is O(M) , which is very much less than O(N log2 N) of the FFT algorithms. This is because M, which denotes the number of nonzero peakcancelling samples, is much less than N, the FFT window size. The proposed method was found to achieve high PAPR reductions while utilizing only a few nonzero peak-cancelling samples and it does not significantly change the power of the transmitted signal. For example, with M=5% of 256-point IFFT samples, corresponding to a data rate loss of 4.8%, a large PAPR reduction of 5.9 dB could be achieved at a small power loss of 0.09 dB. Compared with other methods proposed in literature, the proposed method was found to outperform them in terms of PAPR reductions and BER performance.

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