scholarly journals Uplink Achievable Rate Maximization for Reconfigurable Intelligent Surface Aided Millimeter Wave Systems with Resolution-Adaptive ADCs

2021 ◽  
pp. 1-1
Author(s):  
Yue Xiu ◽  
Jun Zhao ◽  
Ertugrul Basar ◽  
Marco Di Renzo ◽  
Wei Sun ◽  
...  
Keyword(s):  
Millimeter Wave ◽  
Achievable Rate ◽  
Rate Maximization

Analysis of Optimum Precoding schemes in Millimeter Wave System

2020 ◽  
Vol 13 ◽  
Author(s):  
Divya Singh ◽  
Aasheesh Shukla
Keyword(s):  
Power Consumption ◽  
Millimeter Wave ◽  
Spectral Efficiency ◽  
Phase Shifter ◽  
Phase Shifters ◽  
Achievable Rate ◽  
Wave System ◽  
Digital To Analog Converter ◽  
Hybrid Precoding ◽  
Analog Converter

Background : Millimeter wave technology is the emerging technology in wireless communication due to increased demand for data traffic and its numerous advantages however it suffers from severe attenuation. To mitigate this attenuation, phased antenna arrays are used for unidirectional power distribution. An initial access is needed to make a connection between the base station and users in millimeter wave system. The high complexity and cost can be mitigated by the use of hybrid precoding schemes. Hybrid precoding techniques are developed to reduce the complexity, power consumption and cost by using phase shifters in place of converters. The use of phase shifters also increases the spectral efficiency. Objective: Analysis of Optimum Precoding schemes in Millimeter Wave System. Method: In this paper, the suitability of existing hybrid precoding solutions are explored on the basis of the different algorithms and the architecture to increase the average achievable rate. Previous work done in hybrid precoding is also compared on the basis of the resolution of the phase shifter and digital to analog converter. Results: A comparison of the previous work is done on the basis of different parameters like the resolution of phase shifters, digital to analog converter, amount of power consumption and spectral efficiency. Table 2 shows the average achievable rate of different algorithms at SNR= 0 dB and 5 dB. Table 3 also compares the performance achieved by the hybrid precoder in the fully connected structure with two existing approaches, dynamic subarray structure with and without switch and sub connected or partially connected structure. Table 4 gives the comparative analysis of hybrid precoding with the different resolutions of the phase shifter and DAC. Conclusion: In this paper, some available literature is reviewed and summarized about hybrid precoding in millimeter wave communication. Current solutions of hybrid precoding are also reviewed and compared in terms of their efficiency, power consumption, and effectiveness. The limitations of the existing hybrid precoding algorithms are the selection of group and resolution of phase shifters. The mm wave massive MIMO is only feasible due to hybrid precoding.

scholarly journals Sum-Rate Maximization in Sub-28-GHz Millimeter-Wave MIMO Interfering Networks

2017 ◽  
Vol 35 (7) ◽  
pp. 1649-1662 ◽  
Author(s):  
Hadi Ghauch ◽  
Taejoon Kim ◽  
Mats Bengtsson ◽  
Mikael Skoglund
Keyword(s):  
Millimeter Wave ◽  
Rate Maximization ◽  
Sum Rate Maximization ◽  
Sum Rate

scholarly journals Leveraging Secondary Reflections and Mitigating Interference in Multi-IRS/RIS Aided Wireless Network

2021 ◽  
Author(s):  
Tu Nguyen ◽  
Diep Nguyen ◽  
Marco Di Renzo ◽  
Rui Zhang
Keyword(s):  
Optimization Problems ◽  
Phase Shifts ◽  
Base Station ◽  
Achievable Rate ◽  
Weighted Sum ◽  
Rate Maximization ◽  
Single User ◽  
Sum Rate Maximization ◽  
Weighted Sum Rate ◽  
Weighted Sum Rate Maximization

Reconfigurable surfaces (RS) have recently emerged as an enabler for smart radio environments where they are used to actively tailor/control the radio propagation (e.g., to support users under adverse channel conditions). If multiple RSs are deployed (e.g., coated on various buildings) to support different groups of users, it is critical to jointly optimize the phase-shifts of all RSs to mitigate their interference as well as to leverage the secondary reflections amongst them. Motivated by the above, this paper considers the uplink transmissions of multiple users that are grouped and supported by multiple RSs to communicate with a multi-antenna base station (BS). We first formulate two optimization problems: the weighted sum-rate maximization and the minimum achievable rate (from all users) maximization. Unlike existing works that considered single user or single RS or multiple RSs without inter-RS reflections, the considered problems require one to optimize the phase-shifts of all RSs' elements and all beamformers at the multi-antenna BS. The two problems turn out to be non-convex and thus are difficult to be solved in general. Moreover, the inter-RS reflections give rise to the coupling of the phase-shifts amongst RSs, making the optimization problems even more challenging to solve. To tackle them, we design alternating optimization algorithms that provably converge to locally optimal solutions. Simulation results reveal that by properly managing interference and leveraging the secondary reflections amongst RSs, there is a great benefit of deploying more RSs to support different groups of users and so as to achieve a higher rate per user. This gain is even more significant with a larger number of elements per RS. In contrast, without properly managing the secondary reflections, increasing the number of RSs can adversely impact the network throughput, especially for higher transmit power.<br>

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>

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