Hybrid wideband transceiver design for achievable rate maximization in millimeter-wave MIMO systems

2021 ◽  
Author(s):  
Hafiz Muhammad Tahir Mustafa
Keyword(s):  
Millimeter Wave ◽  
Mimo Systems ◽  
Achievable Rate ◽  
Transceiver Design ◽  
Rate Maximization

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.

scholarly journals Joint Analog Beam Selection and Digital Beamforming in Millimeter Wave Cell-Free Massive MIMO Systems

2021 ◽  
Author(s):  
Cenk M. Yetis ◽  
Emil Björnson ◽  
Pontus Giselsson
Keyword(s):  
Millimeter Wave ◽  
Massive Mimo ◽  
Digital Filters ◽  
Mimo Systems ◽  
Low Cost ◽  
Supervised Machine Learning ◽  
Rate Maximization ◽  
Selection Decisions ◽  
Beam Selection ◽  
Sum Rate

<p>Cell-free massive MIMO systems consist of many distributed access points with simple components that jointly serve the users. In millimeter wave bands, only a limited set of predetermined beams can be supported. In a network that consolidates these technologies, downlink analog beam selection stands as a challenging task for the network sum-rate maximization. Low-cost digital filters can improve the network sum-rate further. In this work, we propose low-cost joint designs of analog beam selection and digital filters. The pro-posed joint designs achieve significantly higher sum-rates than the disjoint design benchmark. Supervised machine learning (ML) algorithms can efficiently approximate the input-output mapping functions of the beam selection decisions of the joint designs with low computational complexities. Since the training of ML algorithms is performed off-line, we pro-pose a well-constructed joint design that combines multiple initializations, iterations, and selection features, as well as beam conflict control, i.e., the same beam cannot be used for multiple users. The numerical results indicate that ML algorithms can retain 99-100% of the original sum-rate results achieved by the proposed well-constructed designs.</p>

scholarly journals Particle Swarm Optimization Based Beamforming in Massive MIMO Systems

2020 ◽  
Vol 14 (05) ◽  
pp. 176
Author(s):  
Thaar A. Kareem ◽  
Maab Alaa Hussain ◽  
Mays Kareem Jabbar
Keyword(s):  
Particle Swarm Optimization ◽  
Millimeter Wave ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Particle Swarm ◽  
Optimal Performance ◽  
Achievable Rate ◽  
Swarm Optimization ◽  
Transmit Precoding

<p>This research puts forth an optimization- based analog beamforming scheme for millimeter-wave (mmWave) massive MIMO systems. Main aim is to optimize the combination of analog precoder / combiner matrices for the purpose of getting near-optimal performance. Codebook-based analog beamforming with transmit precoding and receive combining serves the purpose of compensating the severe attenuation of mmWave signals. The existing and traditional beamforming schemes involve a complex search for the best pair of analog precoder / combiner matrices from predefined codebooks. In this research, we have solved this problem by using Particle Swarm Optimization (PSO) to find the best combination of precoder / combiner matrices among all possible pairs with the objective of achieving near-optimal performance with regard to maximum achievable rate. Experiments prove the robustness of the proposed approach in comparison to the benchmarks considered. <strong></strong></p><p class="IndexTerms"> </p>

scholarly journals Joint Analog Beam Selection and Digital Beamforming in Millimeter Wave Cell-Free Massive MIMO Systems

2021 ◽  
Author(s):  
Cenk M. Yetis ◽  
Emil Björnson ◽  
Pontus Giselsson
Keyword(s):  
Millimeter Wave ◽  
Massive Mimo ◽  
Digital Filters ◽  
Mimo Systems ◽  
Low Cost ◽  
Supervised Machine Learning ◽  
Rate Maximization ◽  
Selection Decisions ◽  
Beam Selection ◽  
Sum Rate

<p>Cell-free massive MIMO systems consist of many distributed access points with simple components that jointly serve the users. In millimeter wave bands, only a limited set of predetermined beams can be supported. In a network that consolidates these technologies, downlink analog beam selection stands as a challenging task for the network sum-rate maximization. Low-cost digital filters can improve the network sum-rate further. In this work, we propose low-cost joint designs of analog beam selection and digital filters. The pro-posed joint designs achieve significantly higher sum-rates than the disjoint design benchmark. Supervised machine learning (ML) algorithms can efficiently approximate the input-output mapping functions of the beam selection decisions of the joint designs with low computational complexities. Since the training of ML algorithms is performed off-line, we pro-pose a well-constructed joint design that combines multiple initializations, iterations, and selection features, as well as beam conflict control, i.e., the same beam cannot be used for multiple users. The numerical results indicate that ML algorithms can retain 99-100% of the original sum-rate results achieved by the proposed well-constructed designs.</p>

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