| 1. |
- Becirovic, Ema, 1992-
(författare)
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Signal Processing Aspects of Massive MIMO
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Massive MIMO (multiple-input-multiple-output) is a technology that uses an antenna array with a massive number of antennas at the wireless base station. It has shown widespread benefit and has become an inescapable solution for the future of wireless communication. The mainstream literature focuses on cases when high data rates for a handful of devices are of priority. In reality, due to the diversity of applications, no solution is one-size-fits-all. This thesis provides signal-processing solutions for three challenging situations. The first challenging situation deals with the acquisition of channel estimates when the signal-to-noise-ratio (SNR) is low. The benefits of massive MIMO are unlocked by having good channel estimates. By the virtue of reciprocity in time-division duplex, the estimates are obtained by transmitting pilots on the uplink. However, if the uplink SNR is low, the quality of the channel estimates will suffer and consequently the spectral efficiency will also suffer. This thesis studies two cases where the channel estimates can be improved: one where the device is stationary such that the channel is constant over many coherence blocks and one where the device has access to accurate channel estimates such that it can design its pilots based on the knowledge of the channel. The thesis provides algorithms and methods that exploit the aforementioned structures which improve the spectral efficiency. Next, the thesis considers massive machine-type communications, where a large number of simple devices, such as sensors, are communicating with the base station. This thesis provides a quantitative study on which type of benefits massive MIMO can provide for this communication scenario — many devices can be spatially multiplexed and their battery life can be increased. Further, activity detection is also studied and it is shown that the channel hardening and favorable propagation properties of massive MIMO can be exploited to design efficient detection algorithms. The third part of the thesis studies a more specific application of massive MIMO, namely federated learning. In federated learning, the goal is for the devices to collectively train a machine learning model based on their local data by only transmitting model updates to the base station. Sum channel estimation has been advocated for blind over-the-air federated learning since fewer communication resources are required to obtain such estimates. On the contrary, this thesis shows that individually estimating each device's channel can save a huge number of resources owing to the fact that it allows for individual processing such as gradient sparsification which in turn saves a huge number of resources that compensates for the channel estimation overhead.
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| 2. |
- Ghazanfari, Amin, 1983-
(författare)
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Multi-Cell Massive MIMO: Power Control and Channel Estimation
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Cellular network operators have witnessed significant growth in data traffic in the past few decades. This growth occurs due to the increase in the number of connected mobile devices, and further, the emerging mobile applications developed for rendering video-based on-demand services. As the available frequency bandwidth for cellular communication is limited, significant efforts are dedicated to improving the utilization of available spectrum and increasing the system performance with the aid of new technologies. Third-generation (3G) and fourth-generation (4G) mobile communication networks were designed to facilitate high data traffic in cellular networks in past decades. Nevertheless, there is still a requirement for new cellular network technologies to accommodate the ever-growing data traffic demand. The fifth-generation (5G) is the latest generation of mobile communication systems deployed and implemented around the world. Its objective is to meet the tremendous ongoing increase in the data traffic requirements in cellular networks. Massive MIMO (multiple-input-multi-output) is one of the backbone technologies in 5G networks. Massive MIMO originated from the concept of multi-user MIMO. It consists of base stations (BSs) implemented with a large number of antennas to increase the signal strengths via adaptive beamforming and concurrently serving many users on the same time-frequency blocks. With Massive MIMO technology, there is a notable enhancement of both sum spectral efficiency (SE) and energy efficiency (EE) in comparison with conventional MIMO-based cellular networks. Resource allocation is an imperative factor to exploit the specified gains of Massive MIMO. It corresponds to efficiently allocating resources in the time, frequency, space, and power domains for cellular communication. Power control is one of the resource allocation methods of Massive MIMO networks to deliver high spectral and energy efficiency. Power control refers to a scheme that allocates transmit powers to the data transmitters such that the system maximizes some desirable performance metric. The first part of this thesis investigates reusing a Massive MIMO network's resources for direct communication of some specific user pairs known as device-to-device (D2D) underlay communication. D2D underlay can conceivably increase the SE of traditional Massive MIMO networks by enabling more simultaneous transmissions on the same frequencies. Nevertheless, it adds additional mutual interference to the network. Consequently, power control is even more essential in this scenario than the conventional Massive MIMO networks to limit the interference caused by the cellular network and the D2D communication to enable their coexistence. We propose a novel pilot transmission scheme for D2D users to limit the interference on the channel estimation phase of cellular users compared with sharing pilot sequences for cellular and D2D users. We also introduce a novel pilot and data power control scheme for D2D underlaid Massive MIMO networks. This method aims to assure that the D2D communication enhances the SE of the network compared to conventional Massive MIMO networks. In the second part of this thesis, we propose a novel power control approach for multi-cell Massive MIMO networks. The proposed power control approach solves the scalability issue of two well-known power control schemes frequently used in the Massive MIMO literature, based on the network-wide max-min and proportional fairness performance metrics. We first identify the scalability issue of these existing approaches. Additionally, we provide mathematical proof for the scalability of our proposed method. Our scheme aims at maximizing the geometric mean of the per-cell max-min SE. To solve the optimization problem, we prove that it can be rewritten in a convex form and is solved using standard optimization solvers. The final part of this thesis focuses on downlink channel estimation in a Massive MIMO network. In Massive MIMO networks, to fully benefit from large antennas at the BSs and perform resource allocation, the BS must have access to high-quality channel estimates that can be acquired via the uplink pilot transmission phase. Time-division duplex (TDD) based Massive MIMO relies on channel reciprocity for the downlink transmission. Thanks to the channel hardening in the Massive MIMO networks with ideal propagation conditions, users rely on the statistical knowledge of channels for decoding the data in the downlink. However, when the channel hardening level is low, using only the channel statistics causes fluctuations in the performance. We investigate how to improve the performance by empowering the user to estimate the downlink channel from downlink data transmissions utilizing a model-based and a data-driven approach instead of relying only on channel statistics. Furthermore, the performance of the proposed method is compared with solely relying on statistical knowledge.
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| 3. |
- Ghazanfari, Amin, 1983-
(författare)
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Power Control for Multi-Cell Massive MIMO
- 2019
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The cellular network operators have witnessed significant growth in data traffic in the past few decades. This growth occurs due to the increases in the number of connected mobile devices, and further, the emerging mobile applications developed for rendering video-based on-demand services. As the frequency bandwidth for cellular communication is limited, significant effort was dedicated to improve the utilization of the available spectrum and increase the system performance via new technologies. For example, 3G and 4G networks were designed to facilitate high data traffic in cellular networks in past decades. Nevertheless, there is a necessity for new cellular network technologies to accommodate the ever-growing data traffic demand. 5G is behind the corner to deal with the tremendous data traffic requirements that will appear in cellular networks in the next decade.Massive MIMO (multiple-input-multi-output) is one of the backbone technologies in 5G networks. Massive MIMO originated from the concept of multi-user MIMO. It consists of base stations (BSs) implemented with a large number of antennas to increase the signal strengths via adaptive beamforming and concurrently serving many users on the same time-frequency blocks. As an outcome of using Massive MIMO technology, there is a notable enhancement of both sum spectral efficiency (SE) and energy efficiency (EE) in comparison with conventional MIMO based cellular networks. Resource allocation is an imperative factor to exploit the specified gains of Massive MIMO. It corresponds to properly allocating resources in the time, frequency, space, and power domains for cellular communication. Power control is one of the resource allocation methods to deliver high spectral and energy efficiency of Massive MIMO networks. Power control refers to a scheme that allocates transmit powers to the data transmitters such that the system maximizes some desirable performance metric.In the first part of this thesis, we investigate reusing the resources of a Massive MIMO system, for direct communication of some specific user pairs known as device-to-device (D2D) underlay communication. D2D underlay can conceivably increase the SE of traditional Massive MIMO systems by enabling more simultaneous transmissions on the same frequencies. Nevertheless, it adds additional mutual interference to the network. Consequently, power control is even more essential in this scenario in comparison with conventional Massive MIMO systems to limit the interference that is caused between the cellular network and the D2D communication, thereby enabling their coexistence. In this part, we propose a novel pilot transmission scheme for D2D users to limit the interference to the channel estimation phase of cellular users in comparison with the case of sharing pilot sequences for cellular and D2D users. We also introduce a novel pilot and data power control scheme for D2D underlaid Massive MIMO systems. This method aims at assuring that D2D communication enhances the SE of the network in comparison with conventional Massive MIMO systems.In the second part of this thesis, we propose a novel power control approach for multi-cell Massive MIMO systems. The new power control approach solves the scalability issue of two well-known power control schemes frequently used in the Massive MIMO literature, which are based on the network-wide max-min and proportional fairness performance metrics. We first explain the scalability issue of these existing approaches. Additionally, we provide mathematical proof for the scalability of our proposed method. Our scheme aims at maximizing the geometric mean of the per-cell max-min SE. To solve this optimization problem, we prove that it can be rewritten in a convex form and then be solved using standard optimization solvers.
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| 4. |
- Hu, Chung-Hsuan, 1988-
(författare)
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Communication-Efficient Resource Allocation for Wireless Federated Learning Systems
- 2023
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The training of machine learning (ML) models usually requires a massive amount of data. Nowadays, the ever-increasing number of connected user devices has benefited the development of ML algorithms by providing large sets of data that can be utilized for model training. As privacy concerns become vital in our society, using private data from user devices for training ML models becomes tricky. Therefore, federated learning (FL) with on-device information processing has been proposed for its advantages in preserving data privacy. FL is a collaborative ML framework where multiple devices participate in training a common global model based on locally available data. Unlike centralized ML architecture wherein the entire set of training data need to be centrally stored, in an FL system, only model parameters are shared between user devices and a parameter server. Federated Averaging (FedAvg) is one of the most representative and baseline FL algorithms, with an iterative process of model broadcasting, local training, and model aggregation. In every iteration, the model aggregation process can start only when all the devices have finished local training. Thus, the duration of one iteration is limited by the slowest device, which is known as the straggler issue. To resolve this commonly observed issue in synchronous FL methods, altering the synchronous procedure to an asynchronous one has been explored in the literature; that is, the server does not need to wait for all the devices to finish local training before conducting updates aggregation. However, to avoid high communication costs and implementation complexity that the existing asynchronous FL methods have brought in, we alternatively propose a new asynchronous FL framework with periodic aggregation. Since the FL process involves information exchanges over a wireless medium, allowing partial participation of devices in transmitting model updates is a common approach to avoid the communication bottleneck. We thus further develop channel-aware data-importance-based scheduling policies, which are theoretically motivated by the convergence analysis of the proposed FL system. In addition, an age-aware aggregation weighting design is proposed to deal with the model update asynchrony among scheduled devices in the considered asynchronous FL system. The effectiveness of the proposed scheme is empirically proved of alleviating the straggler effect and achieving better learning outcomes compared to some state-of-the-art methods. From the perspective of jointly optimizing system efficiency and learning performance, in the rest of the thesis, we consider a scenario of Federated Edge Learning (FEEL) where in addition to the heterogeneity of data and wireless channels, heterogeneous computation capability and energy availability are also taken into account in the scheduling design. Besides, instead of assuming all the local data are available at the beginning of the training process, a more practical scenario where the training data might be generated randomly over time is considered. Hence, considering time-varying local training data, wireless link condition, and computing capability, we formulate a stochastic network optimization problem and propose a dynamic scheduling algorithm for optimizing the learning performance subject to per-round latency requirement and long-term energy constraints. The effectiveness of the proposed design is validated by numerical simulations, showing gains in learning performance and system efficiency compared to alternative methods.
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| 5. |
- Kunnath Ganesan, Unnikrishnan, 1989-
(författare)
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Distributed Massive MIMO : Random Access, Extreme Multiplexing and Synchronization
- 2022
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The data traffic in wireless networks has grown tremendously over the past few decades and is ever-increasing. Moreover, there is an enormous demand for speed as well. Future wireless networks need to support three generic heterogeneous services: enhanced mobile broadband(eMBB), ultra-reliable low latency communication (URLLC) and massive machine type communication (mMTC). Massive MIMO has shown to be a promising technology to meet the demands and is now an integral part of 5G networks. To get high data rates, ultra densification of the network by deploying more base stations in the same geographical area is considered. This led to an increase in inter-cell interference which limits the capacity of the network. To mitigate the inter-cell interference, distributed MIMO is advocated. Cell-free massive MIMO is a promising technology to improve the capacity of the network. It leverages all the benefits from ultra densification, massive MIMO, and distributed MIMO technologies and operates without cell boundaries. In this thesis, we study random access, extreme multiplexing capabilities, and synchronization aspects of distributed massive MIMO. In Paper A studies the activity detection in grant-free random access for mMTC in cell-free massive MIMO network. An algorithm is proposed for activity detection based on maximum likelihood detection and the results show that the macro-diversity gain provided by the cell-free architecture improves the activity detection performance compared to co-located architecture when the coverage area is large. RadioWeaves technology is a new wireless infrastructure devised for indoor applications leveraging the benefits of massive MIMO and cell-free massive MIMO. In Paper B, we study the extreme multiplexing capabilities of RadioWeaves which can provide high data rates with very low power. We observe that the RadioWeaves deployment can spatially separate users much better than a conventional co-located deployment, which outweighs the losses caused by grating lobes and thus saves a lot on transmit power. Paper C studies the synchronization aspect of distributed massive MIMO. We propose a novel, over-the-air synchronization protocol, which we call as BeamSync, to synchronize all the different multi-antenna transmit panels. We also show that beamforming the synchronization signal in the dominant direction of the channel between the panels is optimal and the synchronization performance is significantly better than traditional beamforming techniques.
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| 6. |
- Shaik, Zakir Hussain
(författare)
-
Cell-Free Massive MIMO: Distributed Signal Processing and Energy Efficiency
- 2022
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In this era of rapid wireless technological advancements, wireless connectivity between humans, humans with machines, and machines with machines is gradually becoming an absolute necessity. The initial motivation for wireless connectivity was to enable voice communication between humans over a geo-graphical area. Thanks to cellular communications advancements in the past decade, cellular wireless connectivity has become a global success, starting from 1G to the present generation 5G. However, the needs of humans often evolve with time, and now the world is witnessing an ever-growing demand for the internet with high data rates besides reliable voice communication. Current cellular networks suffer from non-uniform data rates across a cell, i.e., users at the cell center and the cell edges experience significant variations in signal-to-noise ratio, making the cellular technology less reliable to meet the future data demands. Moreover, cellular networks operating as cells, i.e., an access point (AP, the term we would use instead of base station) serving the users within its geographical location, cannot leverage the network’s total capacity without cooperation among APs of the neighboring cells. One potential solution is moving away from the cell to cell-free networks wherein all the APs will serve all the users within the geographical coverage area. Thus, there is a need for a paradigm shift in how cellular networks operate. Towards the goal mentioned above to fully leverage the network capacity, the Cell-Free Massive multiple-input-multiple-output (MIMO) technology is expected to be the next potential technology beyond 5G combining the benefits of Massive MIMO and cell-free distributed architectures. Distributed architectures require distributed signal processing algorithms, and also energy consumption of the network is crucial. Keeping in view the practical ease in deployment, we consider a sequentially connected Cell-Free Massive MIMO network called a “radio stripe”. In the first part of the thesis, we focus on developing an optimal sequential algorithm in the sense of mean-square-error (MSE) which has the same performance as that of centralized Cell-Free Massive MIMO implementation with the minimum MSE (MMSE) receiver. We also develop an optimal sequential algorithm that decentralizes the centralized bit LLR computation. Another attractive aspect of these proposed algorithms is that the fronthaul (number of real symbols required by the central processing unit (CPU) to decode the transmitted signal) is independent of the number of APs. On the contrary, centralized implementation fronthaul is dependent on the number of APs, causing scalability problems with the increase in APs. In the second part of the thesis, we develop an algorithm focused on maximizing the energy efficiency of the RadioWeave network in an underlay spectrum sharing. RadioWeave is a technology envisioned to combine Cell-Free Massive MIMO and possibly large intelligent surfaces. We first present the energy efficiency problem, which is non-convex in its original form. Then, a convex lower bound on the problem is provided with an iterative algorithm to solve the problem efficiently.
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| 7. |
- Van Chien, Trinh, 1989-
(författare)
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Spatial Resource Allocation in Massive MIMO Communications : From Cellular to Cell-Free
- 2020
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Massive MIMO (multiple-input multiple-output) is considered as an heir of the multi-user MIMO technology and it has gained lots of attention from both academia and industry since the last decade. By equipping base stations (BSs) with hundreds of antennas in a compact array or a distributed manner, this new technology can provide very large multiplexing gains by serving many users on the same time-frequency resources and thereby bring significant improvements in spectral efficiency (SE) and energy efficiency (EE) over the current wireless networks. The transmit power, pilot training, and spatial transmission resources need to be allocated properly to the users to achieve the highest possible performance. This is called resource allocation and can be formulated as design utility optimization problems. If the resource allocation in Massive MIMO is optimized, the technology can handle the exponential growth in both wireless data traffic and number of wireless devices, which cannot be done by the current cellular network technology.In this thesis, we focus on the five different resource allocation aspects in Massive MIMO communications: The first part of the thesis studies if power control and advanced coordinated multipoint (CoMP) techniques are able to bring substantial gains to multi-cell Massive MIMO systems compared to the systems without using CoMP. More specifically, we consider a network topology with no cell boundary where the BSs can collaborate to serve the users in the considered coverage area. We focus on a downlink (DL) scenario in which each BS transmits different data signals to each user. This scenario does not require phase synchronization between BSs and therefore has the same backhaul requirements as conventional Massive MIMO systems, where each user is preassigned to only one BS. The scenario where all BSs are phase synchronized to send the same data is also included for comparison. We solve a total transmit power minimization problem in order to observe how much power Massive MIMO BSs consume to provide the requested quality of service (QoS) of each user. A max-min fairness optimization is also solved to provide every user with the same maximum QoS regardless of the propagation conditions.The second part of the thesis considers a joint pilot design and uplink (UL) power control problem in multi-cell Massive MIMO. The main motivation for this work is that the pilot assignment and pilot power allocation is momentous in Massive MIMO since the BSs are supposed to construct linear detection and precoding vectors from the channel estimates. Pilot contamination between pilot-sharing users leads to more interference during data transmission. The pilot design is more difficult if the pilot signals are reused frequently in space, as in Massive MIMO, which leads to greater pilot contamination effects. Related works have only studied either the pilot assignment or the pilot power control, but not the joint optimization. Furthermore, the pilot assignment is usually formulated as a combinatorial problem leading to prohibitive computational complexity. Therefore, in the second part of this thesis, a new pilot design is proposed to overcome such challenges by treating the pilot signals as continuous optimization variables. We use those pilot signals to solve different max-min fairness optimization problems with either ideal hardware or hardware impairments.The third part of this thesis studies a two-layer decoding method that mitigates inter-cell interference in multi-cell Massive MIMO systems. In layer one, each BS estimates the channels to intra-cell users and uses the estimates for local decoding within the cell. This is followed by a second decoding layer where the BSs cooperate to mitigate inter-cell interference. An UL achievable SE expression is computed for arbitrary two-layer decoding schemes, while a closed form expression is obtained for correlated Rayleigh fading channels, maximum-ratio combining (MRC), and largescale fading decoding (LSFD) in the second layer. We formulate a sum SE maximization problem with both the data power and LSFD vectors as optimization variables. Since the problem is non-convex, we develop an algorithm based on the weighted minimum mean square error (MMSE) approach to obtain a stationary point with low computational complexity.Motivated by recent successes of deep learning in predicting the solution to an optimization problem with low runtime, the fourth part of this thesis investigates the use of deep learning for power control optimization in Massive MIMO. We formulate the joint data and pilot power optimization for maximum sum SE in multi-cell Massive MIMO systems, which is a non-convex problem. We propose a new optimization algorithm, inspired by the weighted MMSE approach, to obtain a stationary point in polynomial time. We then use this algorithm together with deep learning to train a convolutional neural network to perform the joint data and pilot power control in sub-millisecond runtime. The solution is suitable for online optimization.Finally, the fifth part of this thesis considers a large-scale distributed antenna system that serves the users by coherent joint transmission called Cell-free Massive MIMO. For a given user set, only a subset of the access points (APs) is likely needed to satisfy the users' performance demands. To find a flexible and energy-efficient implementation, we minimize the total power consumption at the APs in the DL, considering both the hardware consumed and transmit powers, where APs can be turned off to reduce the former part. Even though this is a nonconvex optimization problem, a globally optimal solution is obtained by solving a mixed-integer second-order cone program (SOCP). We also propose low-complexity algorithms that exploit group-sparsity or received power strength in the problem formulation.
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| 8. |
- Özdogan, Özgecan, 1992-
(författare)
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Analysis of Cellular and Cell-Free Massive MIMO with Rician Fading
- 2020
-
Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The data traffic in cellular networks has grown at an exponential pace for decades. This trend will most probably continue in the future, driven by new innovative applications. One of the key enablers of future cellular networks is the massive MIMO technology. A massive MIMO base station is equipped with a massive number (e.g., a hundred) of individually steerable antennas, which can be effectively used to serve tens of user equipments simultaneously on the same time-frequency resource. It can provide a notable enhancement of both spectral efficiency and energy efficiency in comparison with conventional MIMO.In the literature, the achievable spectral efficiencies of massive MIMO systems with a practical number of antennas have been rigorously characterized and optimized when the channels are subject to either spatially uncorrelated or correlated Rayleigh fading. Typically, in massive MIMO research, i.i.d. Rayleigh fading or less frequently free-space line-of-sight (LoS) channel models are assumed since they simplify the analysis. Massive MIMO technology is able to support both rich scattering and LoS scenarios. However, practical channels can consist of a combination of an LoS path and a correlated small-scale fading component caused by a finite number of scattering clusters that can be modeled by spatially correlated Rician fading. In the first part of this thesis, we consider a multi-cell scenario with spatially correlated Rician fading channels and derive closed-form achievable spectral efficiency expressions for different signal processing techniques.Alternatively, a massive number of antennas can be spread over a large geographical area and this concept is called cell-free massive MIMO. In the canonical form of cell-free massive MIMO, the access points cooperate via a fronthaul network to spatially multiplex the users on the same time-frequency resource using network MIMO methods that only require locally obtained channel state information. Cellfree massive MIMO is a densely deployed system. Hence, the probability of having an LoS path between some access points and the users is quite high. In the second part of this thesis, we consider a practical scenario where the channels between the access points and the users are modeled with Rician fading.
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| 9. |
- Özdogan, Özgecan, 1992-
(författare)
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Signal Processing Aspects of Massive MIMO and IRS-Aided Communications
- 2022
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The data traffic in cellular networks has grown at an exponential pace for decades. This trend will most probably continue in the future, driven by new innovative applications. One of the key enablers of future cellular networks is the massive MIMO technology, and it has been started to be commercially deployed in many countries. A massive MIMO base station is equipped with a massive number (e.g., a hundred) of individually steerable antennas, which can be effectively used to serve tens of user equipments simultaneously on the same time-frequency resource. It can provide a notable enhancement of both spectral efficiency and energy efficiency in comparison with conventional MIMO. In the prior literature, the achievable spectral efficiencies of massive MIMO systems with a practical number of antennas have been rigorously characterized and optimized when the channels are subject to either spatially uncorrelated or correlated Rayleigh fading. Typically, in massive MIMO research, i.i.d. Rayleigh fading or less frequently free-space line-of-sight (LoS) channel models are assumed since they simplify the analysis. Massive MIMO technology is able to support both rich scattering and LoS scenarios. Practical channels can consist of a combination of an LoS path and a correlated small-scale fading component caused by a finite number of scattering clusters that can be modeled by spatially correlated Rician fading. In Paper \ref{PaperA}, we consider a multi-cell scenario with spatially correlated Rician fading channels and derive closed-form achievable spectral efficiency expressions for different signal processing techniques. Alternatively, a massive number of antennas can be spread over a large geographical area and this concept is called cell-free massive MIMO. In the canonical form of cell-free massive MIMO, the access points cooperate via a fronthaul network to spatially multiplex the users on the same time-frequency resource using network MIMO methods that only require locally obtained channel state information. Cell-free massive MIMO is a densely deployed system. Hence, the probability of having an LoS path between some access points and the users is quite high. In Paper B, we consider a practical scenario where the channels between the access points and the users are modeled with Rician fading. The main theory for massive MIMO has been developed for uni-polarized single-antenna users. Wireless signals are polarized electromagnetic waves, and there exist two orthogonal polarization dimensions. The practical base stations and user equipments typically utilize dual-polarized antennas (i.e., two co-located antennas that respond to orthogonal polarizations) to squeeze in twice the number of antennas in the same physical enclosure, as well as capturing signal components from both dimensions. In Paper C, we study a single-cell massive MIMO system with dual-polarized antennas at both the base station and users. The channel modeling for dual-polarized channels is substantially more complicated than for conventional uni-polarized channels. A channel model that takes into account several practical aspects that arise when utilizing dual-polarization, such as channel cross-polar discrimination (XPD) and cross-polar receive and transmit correlations (XPC) is considered. Another technology that has exciting prospects and is quickly gaining traction in wireless communications is intelligent reflecting surfaces (IRS). It is also known under the names reconfigurable intelligent surfaces and software-controlled metasurfaces. IRS is a thin two-dimensional metasurface that is used to aid communications. According to the application of interest, an IRS has the ability to control and transform electromagnetic waves that are impinging on it. In this thesis, we study different aspects of this technology such as pathloss modeling, channel estimation, and different technology use cases. In Paper D, we derive the pathloss model using physical optics techniques for an IRS that is configured to reflect an incoming wave from a far-field source towards a receiver in the far-field. In Paper E, we demonstrate how an IRS can be used to increase the rank of the channel matrix in LoS point-to-point MIMO communications by creating a controllable path that complements the uncontrollable paths. Bringing IRS technology into reality requires addressing many practical challenges. For instance, the proper configuration of an IRS critically depends on accurate channel state information. However, there are two main issues that complicate the channel acquisition with IRS. First, the IRS is not inherently equipped with transceiver chains. Therefore, it can not sense the pilot signals. Besides, introducing an IRS into an existing setup will increase the number of channel coefficients proportionally to the number of IRS elements. In Paper F, we present a deep learning-based approach for phase reconfiguration at an IRS in order to learn and make use of the local propagation environment.
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| 10. |
- Becirovic, Ema, 1992-
(författare)
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On Massive MIMO for Massive Machine-Type Communications
- 2020
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- To cover all the needs and requirements of mobile networks in the future, the predicted usage of the mobile networks has been split into three use-cases: enhanced mobile broadband, ultra-reliable low-latency communication, and massive machine-type communication. In this thesis we focus on the massive machine-type communication use-case which is intended to facilitate the ever increasing number of smart devices and sensors.In the massive machine-type communication use-case, the main challenges are to accommodate a huge number of devices while keeping the battery lives of the devices long, and allowing them to be placed in far-away locations. However, these devices are not concerned about other features such as latency, high data rate, or mobility.In this thesis we study the application of massive MIMO (multiple-input multiple-output) technology for the massive machine-type communication use-case. Massive MIMO has been on the radar as an enabler for future communication networks in the last decade and is now firmly rooted in both academia and industry. The main idea of massive MIMO is to utilize a base station with a massive number of antennas which gives the ability to spatially direct signals and serve multiple devices in the same time- and frequency resource.More specifically, in this thesis we study A) a scenario where the base station takes advantage of a device's low mobility to improve its channel estimate, B) a random access scheme for massive machine-type communication which can accommodate a huge number of devices, and C) a case study where the benefits of massive MIMO for long range devices are quantified. The results are that the base station can significantly improve the channel estimates for a low mobility user such that it can tolerate lower SNR while still achieving the same rate. Additionally, the properties of massive MIMO greatly helps to detect users in random access scenarios and increase link-budgets compared to single-antenna base stations.
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