95 research outputs found
User-Centric Cell-Free Massive Mimo With Ris-Integrated Antenna Arrays
No full textCell-free massive MIMO (multiple-input multiple-output) is a promising network architecture for beyond 5G systems, which can particularly offer more uniform data rates across the coverage area. Recent works have shown how reconfigurable intelligent surfaces (RISs) can be used as relays in cell-free massive MIMO networks to improve data rates further. In this paper, we analyze an alternative architecture where an RIS is integrated into the antenna array at each access point and acts as an intelligent transmitting surface to expand the aperture area. This approach alleviates the multiplicative fading effect that normally makes RIS-aided systems inefficient and offers a cost-effective alternative to building large antenna arrays. We use a small number of antennas and a larger number of controllable RIS elements to match the performance of an antenna array whose size matches that of the RIS. In this paper, we explore this innovative transceiver architecture in the uplink of a cell-free massive MIMO system for the first time, demonstrating its potential benefits through analytic and numerical contributions. The simulation results validate the effectiveness of our proposed phase-shift configuration and highlight scenarios where the proposed architecture significantly enhances data rates.The work by O. T. Demir was supported by 2232-B International Fellowship for Early Stage Researchers Programme funded by the Scientific and Technological Research Council of Turkiye. E. Bjornson was supported by the FFL18-0277 grant from the Swedish Foundation for Strategic Research.Scientific and Technological Research Council of Turkiye; Swedish Foundation for Strategic Research [FFL18-0277
Datasets associated with the article "Learning-Based Downlink Power Allocation in Cell-Free Massive MIMO Systems" published in IEEE Transactions on Wireless Communications
No full textThe datasets are associated with the non-orthogonal pilot assignment case in the article. The input and labelled output for training the models therein, and the computed SE performance for the conventional optimization approaches are provided.
A brief description of the datasets:
'APpositions.npy': Represents the access point (AP) 2D locations utilized to generate the datasets.
'dataset_betas.npy': Large-scale fading coefficients in linear scale [W]. Represents the input to the DNN models.
'dataset_mu_XX_WMMSE_ADMM.npy': Locally optimal square roots of power coefficients [sqrt(W)] for the sum-SE maximization objective with; (1) XX = MR, and (2) XX = RZF precoding schemes. Represents the labelled output of the DNN models.
'dataset_mu_XX_WMMSE_PF_ADMM.npy': Locally optimal square roots of power coefficients [sqrt(W)] for the proportional fairness (PF) maximization objective with; (1) XX = MR, and (2) XX = RZF precoding schemes. Represents the labelled output of the DNN models.
'dataset_SE_XX_WMMSE_ADMM.npy': Per user spectral efficiency (SE) in [bits/s/Hz] for the sum-SE maximization objective with ; (1) XX = MR, and (2) XX = RZF precoding schemes.
'dataset_SE_XX_WMMSE_PF_ADMM.npy': Per user spectral efficiency (SE) in [bits/s/Hz] for the proportional fairness (PF) maximization objective with ; (1) XX = MR, and (2) XX = RZF precoding schemes.
If you in anyway use this code for research that results in publications, please cite our original article listed below.
The article can be found at: 10.1109/TWC.2022.3192203. Also on arXiv at: https://arxiv.org/pdf/2109.03128.pdf.
The simulation code is available here on GitHub for training and testing the DNN models
Dışbükey olmayan problemler için optimizasyon teknikleri ve optimum ayrık verici hüzme tasarımı.
No full textIn this thesis, transmit beamformer design is investigated for single group multicast scenario. The problem is considered for both discrete and continuous case. The discrete problem is converted to a linear form in which there are both discrete and continuous variables. The resulting mixed integer linear programming problem is optimally solved with much lower computational complexity than brute force search. For practical reasons, robust version of the problem is also elaborated and solved with mixed integer convex programming. Several experiments are carried out in order to show performance gain and computational complexity of the proposed techniques. An important variation of discrete beamforming problem for spectrum sharing based cognitive radio is also considered. Antenna and secondary user selection which are critical in cognitive radio scenario are included into this beamforming problem. An equivalent problem to this joint problem is obtained and solved optimally using mixed integer linear programming. It is shown that antenna selection provides the system with power gain and more user service capability. Finally, a near-optimal continuous broadcast beamforming algorithm based on alternating maximization is developed and its performance is shown to be better than the existing approaches in simulation results.M.S. - Master of Scienc
Introduction to Multiple Antenna Communications and Reconfigurable Surfaces
No full textWireless communication is the backbone of the digitized society, where everything is connected and intelligent. Access points and devices are nowadays equipped with multiple antennas to achieve higher data rates, better reliability, and support more users than in the past. This book gives a gentle introduction to multiple antenna communications with a focus on system modeling, channel capacity theory, algorithms, and practical implications. The basics of wireless localization, radar sensing, and controllable reflection through reconfigurable surfaces are also covered. The goal is to provide the reader with a solid understanding of this transformative technology that changes how wireless networks are designed and operated, today and in the future. The first three chapters cover the fundamentals of wireless channels, and the main benefits of using multiple antennas are identified: beamforming, diversity, and spatial multiplexing. The theory and signal processing algorithms for multiple-input multiple-output (MIMO) communications with antenna arrays at the transmitter and receiver are progressively developed. The next two chapters utilize these results to study point-to-point MIMO channels under line-of-sight (LOS) and non-LOS conditions, covering the shape of signal beams, impact of array geometry, polarization, and ways to achieve reliable communication over fading channels. The book then shifts focus to multi-user MIMO channels, where interference between devices is managed by spatial processing. The next chapter extends the theory to multicarrier channels and explains practical digital, analog, and hybrid hardware implementations. The last two chapters cover the role of multiple antennas in localization and sensing, and how reconfigurable surfaces can improve both communication and sensing systems. The text was developed as the textbook for a university course and builds on the reader's previous knowledge of signals and systems, linear algebra, probability theory, and digital communications. Each chapter contains numerous examples, exercises, and simulation results that can be reproduced using accompanying code
Beamforming for energy harvesting and multi-user communications
No full textIn this thesis, several optimization problems are considered related to beamforming for energy harvesting and multi-user communications. In multi-user communications scenarios, physical layer multi-group multicasting systems are considered where there are multiple groups of users who are interested in common information signals. In energy harvesting related scenarios, different protocols are investigated, namely power splitting and self-energy recycling, respectively. In power splitting mode, the mobile device has a power splitting device and some portion of the received radio frequency power is used for energy harvesting while the remaining part is used for information decoding. In self-energy recycling protocol, a separate receive antenna on the relay uses the transmitted signal as an energy source. The contributions of this thesis can be outlined as follows. First, efficient algorithms are proposed for antenna selection and hybrid beamforming in multi-group multicasting systems. The users have a power splitting device and the joint optimization of transmit beamformers and power splitting ratios is considered. Multi-group multicasting is also used for OFDM systems where users harvest energy from some portion of the received signal. The difficult combinatorial problem for the joint optimization of resource allocation and power splitting ratios is solved effectively. In addition, several fast algorithms are proposed for full digital beamforming and two different hybrid beamforming structures with per-antenna power constraints. Apart from multi-group multicasting, relay assisted single user communications is also studied in this thesis. Several scenarios are investigated for energy harvesting relays which use power splitting and self-energy recycling protocols. Both amplify-and-forward and decode-and-forward relaying protocols are considered. For most of the problems, optimum solutions are obtained while for the others, efficient near-optimum solutions are presented
Optimization techniques for nonconvex problems and optimum discrete transmit beamformer design
No full textIn this thesis, transmit beamformer design is investigated for single group multicast scenario. The problem is considered for both discrete and continuous case. The discrete problem is converted to a linear form in which there are both discrete and continuous variables. The resulting mixed integer linear programming problem is optimally solved with much lower computational complexity than brute force search. For practical reasons, robust version of the problem is also elaborated and solved with mixed integer convex programming. Several experiments are carried out in order to show performance gain and computational complexity of the proposed techniques. An important variation of discrete beamforming problem for spectrum sharing based cognitive radio is also considered. Antenna and secondary user selection which are critical in cognitive radio scenario are included into this beamforming problem. An equivalent problem to this joint problem is obtained and solved optimally using mixed integer linear programming. It is shown that antenna selection provides the system with power gain and more user service capability. Finally, a near-optimal continuous broadcast beamforming algorithm based on alternating maximization is developed and its performance is shown to be better than the existing approaches in simulation results
Enerji hasatlı ve çok kullanıcılı iletişim için hüzme şekillendirme.
No full textIn this thesis, several optimization problems are considered related to beamforming for energy harvesting and multi-user communications. In multi-user communications scenarios, physical layer multi-group multicasting systems are considered where there are multiple groups of users who are interested in common information signals. In energy harvesting related scenarios, different protocols are investigated, namely power splitting and self-energy recycling, respectively. In power splitting mode, the mobile device has a power splitting device and some portion of the received radio frequency power is used for energy harvesting while the remaining part is used for information decoding. In self-energy recycling protocol, a separate receive antenna on the relay uses the transmitted signal as an energy source. The contributions of this thesis can be outlined as follows. First, efficient algorithms are proposed for antenna selection and hybrid beamforming in multi-group multicasting systems. The users have a power splitting device and the joint optimization of transmit beamformers and power splitting ratios is considered. Multi-group multicasting is also used for OFDM systems where users harvest energy from some portion of the received signal. The difficult combinatorial problem for the joint optimization of resource allocation and power splitting ratios is solved effectively. In addition, several fast algorithms are proposed for full digital beamforming and two different hybrid beamforming structures with per-antenna power constraints. Apart from multi-group multicasting, relay assisted single user communications is also studied in this thesis. Several scenarios are investigated for energy harvesting relays which use power splitting and self-energy recycling protocols. Both amplify-and-forward and decode-and-forward relaying protocols are considered. For most of the problems, optimum solutions are obtained while for the others, efficient near-optimum solutions are presented.Ph.D. - Doctoral Progra
Foundations of User-Centric Cell-Free Massive MIMO
No full textImagine a coverage area where each mobile device is communicating with a
preferred set of wireless access points (among many) that are selected based on
its needs and cooperate to jointly serve it, instead of creating autonomous
cells. This effectively leads to a user-centric post-cellular network
architecture, which can resolve many of the interference issues and
service-quality variations that appear in cellular networks. This concept is
called User-centric Cell-free Massive MIMO (multiple-input multiple-output) and
has its roots in the intersection between three technology components: Massive
MIMO, coordinated multipoint processing, and ultra-dense networks. The main
challenge is to achieve the benefits of cell-free operation in a practically
feasible way, with computational complexity and fronthaul requirements that are
scalable to enable massively large networks with many mobile devices. This
monograph covers the foundations of User-centric Cell-free Massive MIMO,
starting from the motivation and mathematical definition. It continues by
describing the state-of-the-art signal processing algorithms for channel
estimation, uplink data reception, and downlink data transmission with either
centralized or distributed implementation. The achievable spectral efficiency
is mathematically derived and evaluated numerically using a running example
that exposes the impact of various system parameters and algorithmic choices.
The fundamental tradeoffs between communication performance, computational
complexity, and fronthaul signaling requirements are thoroughly analyzed.
Finally, the basic algorithms for pilot assignment, dynamic cooperation cluster
formation, and power optimization are provided, while open problems related to
these and other resource allocation problems are reviewed. All the numerical
examples can be reproduced using the accompanying Matlab code.Comment: This is the authors' version of the manuscript: \"Ozlem Tugfe Demir,
Emil Bj\"ornson and Luca Sanguinetti (2021), "Foundations of User-Centric
Cell-Free Massive MIMO", Foundations and Trends in Signal Processing: Vol.
14, No. 3-4, pp 162-47
Efficient Channel Estimation With Shorter Pilots in RIS-Aided Communications: Using Array Geometries and Interference Statistics
No full textAccurate estimation of the cascaded channel from a user equipment (UE) to a base station (BS) via each reconfigurable intelligent surface (RIS) element is critical to realizing the full potential of the RIS's ability to control the overall channel. The number of parameters to be estimated is equal to the number of RIS elements, requiring an equal number of pilots unless an underlying structure can be identified. In this paper, we show how the spatial correlation inherent in the different RIS channels provides this desired structure. We first optimize the RIS phase-shift pattern using a much-reduced pilot length (determined by the rank of the spatial correlation matrices) to minimize the mean square error (MSE) in the channel estimation under electromagnetic interference. In addition to considering the linear minimum MSE (LMMSE) channel estimator, we propose a novel channel estimator that requires only knowledge of the array geometry while not requiring any user-specific statistical information. We call this the reduced-subspace least squares (RS-LS) estimator and optimize the RIS phase-shift pattern for it. This novel estimator significantly outperforms the conventional LS estimator. For both the LMMSE and RS-LS estimators, the proposed optimized RIS configurations result in significant channel estimation improvements over the benchmarks
Is Channel Estimation Necessary to Select Phase-Shifts for RIS-Assisted Massive MIMO?
No full textReconfigurable intelligent surfaces (RISs) consist of many passive elements
of metamaterials whose impedance can be controllable to change the
characteristics of wireless signals impinging on them. Channel estimation is a
critical task when it comes to the control of a large RIS when having a channel
with a large number of multipath components. In this paper, we derive Bayesian
channel estimators for two RIS-assisted massive multiple-input multiple-output
(MIMO) configurations: i) the short-term RIS configuration based on the
instantaneous channel estimates; ii) the long-term RIS configuration based on
the channel statistics. The proposed methods exploit spatial correlation
characteristics at both the base station and the planar RISs, and other
statistical characteristics of multi-specular fading in a mobile environment.
Moreover, a novel heuristic for phase-shift selection at the RISs is developed.
A computationally efficient fixed-point algorithm, which solves the max-min
fairness power control optimally, is proposed. Simulation results demonstrate
that the proposed uplink RIS-aided framework improves the spectral efficiency
of the cell-edge mobile user equipments substantially in comparison to a
conventional single-cell massive MIMO system. The impact of several channel
effects are studied to gain insight about when the channel estimation, i.e.,
the short-term configuration, is preferable in comparison to the long-term RIS
configuration to boost the spectral efficiency.Comment: Published in IEEE Transactions on Wireless Communications, vol. 21,
no. 11, November 202
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