scholarly journals Joint Resource Optimization for Orthogonal Frequency Division Multiplexing Based Cognitive Amplify and Forward Relaying Networks

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2074
Author(s):  
Dong Qin ◽  
Tianqing Zhou
Keyword(s):  
Power Allocation ◽  
Orthogonal Frequency Division Multiplexing ◽  
Maximization Problem ◽  
Constraint Condition ◽  
Interference Constraint ◽  
Rate Maximization ◽  
Primary Network ◽  
Secondary Network ◽  
Sum Rate Maximization ◽  
Sum Rate

This paper investigates two resource allocation problems in cognitive relaying networks where both secondary network and primary network coexist in the same frequency band and adopt orthogonal frequency division multiplexing (OFDM) technology. The first one is the sum rate maximization problem of a secondary network under total power budget of a secondary network and tolerable interference constraint of a primary network. The second one is the sum rate maximization problem of a secondary network under separate power budgets of a secondary network and tolerable interference constraint of a primary network. These two optimization problems are completely different from those in traditional cooperative communication due to interference management constraint condition. A joint optimization algorithm is proposed, where power allocation and subcarrier pairing are decomposed into two subproblems with reasonable cost. The first one is a closed form solution of power allocation of the secondary network while managing the interference to a primary network under a constraint condition. The other is optimal subcarrier pairing at given power allocation. Simulation results reveal aspects of average signal to noise ratio (SNR), interference level, relay position, and power ratio on the sum rate of a secondary network.

scholarly journals Sum Rate Maximization for Multi-User Wireless Powered IoT Network With Non-Linear Energy Harvester: Time and Power Allocation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 149698-149710 ◽  
Author(s):  
Tien-Tung Nguyen ◽  
Van-Dinh Nguyen ◽  
Jong-Ho Lee ◽  
Yong-Hwa Kim
Keyword(s):  
Power Allocation ◽  
Energy Harvester ◽  
Rate Maximization ◽  
Non Linear ◽  
Sum Rate Maximization ◽  
Sum Rate

scholarly journals Hybrid Multi-User Precoding with Manifold Discriminative Learning for Millimeter-Wave Massive MIMO Systems

2021 ◽  
pp. 411-422
Author(s):  
Xiaoping Zhou ◽  
◽  
Bin Wang ◽  
Jing Zhang ◽  
Qian Zhang ◽  
...  
Keyword(s):  
Millimeter Wave ◽  
Mimo Systems ◽  
Gradient Methods ◽  
Maximization Problem ◽  
High Signal ◽  
User Clustering ◽  
Rate Maximization ◽  
Sum Rate Maximization ◽  
Sum Rate ◽  
Low Dimensional

In large-array millimeter-wave (mmWave) systems, hybrid multi-user precoding is one of the most attractive research topics. This paper first presents a low-dimensional manifolds architecture for the analog precoder. An objective function is formulated to maximize the Energy Efficiency (EE) in consideration of the insertion loss for hybrid multi-user precoder. The optimal scheme is intractable to achieve, so that we present a user clustering hybrid precoding scheme. By modeling each user set as a manifold, we formulate the problem as clustering-oriented multi-manifolds learning. We discuss the effect of non-ideal factors on the EE performance. Through proper user clustering, the hybrid multi-user precoding is investigated for the sum-rate maximization problem by manifold quasi conjugate gradient methods. The high signal to interference plus noise ratio (SINR) is achieved and the computational complexity is reduced by avoiding the conventional schemes to deal with high-dimensional channel parameters. Performance evaluations show that the proposed scheme can obtain near-optimal sum-rate and considerably higher spectral efficiency than some existing solutions.

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