| 1. |
- Axell, Erik
(författare)
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Spectrum Sensing Algorithms Based on Second-Order Statistics
- 2012
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Cognitive radio is a new concept of reusing spectrum in an opportunistic manner. Cognitive radio is motivated by recent measurements of spectrum utilization, showing unused resources in frequency, time and space. Introducing cognitive radios in a primary network inevitably creates increased interference to the primary users. Secondary users must sense the spectrum and detect primary users' signals at very low SNR, to avoid causing too much interference.This dissertation studies this detection problem, known as spectrum sensing.The fundamental problem of spectrum sensing is to discriminate an observation that contains only noise from an observation that contains a very weak signal embedded in noise. In this work, detectors are derived that exploit known properties of the second-order moments of the signal. In particular, known structures of the signal covariance are exploited to circumvent the problem of unknown parameters, such as noise and signal powers or channel coefficients.The dissertation is comprised of six papers, all in different ways related to spectrum sensing based on second-order statistics. In the first paper, we considerspectrum sensing of orthogonal frequency-division multiplexed (OFDM) signals in an additive white Gaussian noise channel. For the case of completely known noise and signal powers, we set up a vector-matrix model for an OFDM signal with a cyclic prefix and derive the optimal Neyman-Pearson detector from first principles. For the case of completely unknown noise and signal powers, we derive a generalized likelihood ratio test (GLRT) based on empirical second-order statistics of the received data. The proposed GLRT detector exploits the non-stationary correlation structure of the OFDM signal and does not require any knowledge of the noise or signal powers.In the second paper, we create a unified framework for spectrum sensing of signals which have covariance matrices with known eigenvalue multiplicities. We derive the GLRT for this problem, with arbitrary eigenvalue multiplicities under both hypotheses. We also show a number of applications to spectrum sensing for cognitive radio.The general result of the second paper is used as a building block, in the third and fourth papers, for spectrum sensing of second-order cyclostationary signals received at multiple antennas and orthogonal space-time block coded (OSTBC) signals respectively. The proposed detector of the third paper exploits both the spatial and the temporal correlation of the received signal, from knowledge of the fundamental period of the cyclostationary signal and the eigenvalue multiplicities of the temporal covariance matrix.In the fourth paper, we consider spectrum sensing of signals encoded with an OSTBC. We show how knowledge of the eigenvalue multiplicities of the covariance matrix are inherent owing to the OSTBC, and propose an algorithm that exploits that knowledge for detection. We also derive theoretical bounds on the performance of the proposed detector. In addition, we show that the proposed detector is robust to a carrier frequency offset, and propose another detector that deals with timing synchronization using the detector for the synchronized case as a building block.A slightly different approach to covariance matrix estmation is taken in the fifth paper. We consider spectrum sensing of Gaussian signals with structured covariance matrices, and propose to estimate the unknown parameters of the covariance matrices using covariance matching estimation techniques (COMET). We also derive the optimal detector based on a Gaussian approximation of the sample covariance matrix, and show that this is closely connected to COMET.The last paper deals with the problem of discriminating samples that containonly noise from samples that contain a signal embedded in noise, when the variance of the noise is unknown. We derive the optimal soft decision detector using a Bayesian approach. The complexity of this optimal detector grows exponentially with the number of observations and as a remedy, we propose a number of approximations to it. The problem under study is a fundamental one andit has applications in signal denoising, anomaly detection, and spectrum sensing for cognitive radio.
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| 2. |
- 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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| 3. |
- Cheng, Hei Victor
(författare)
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Aspects of Power Allocation in Massive MIMO
- 2016
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The past decades have seen a rapid growth of mobile data trac, both in terms of connected devices and data rate. To satisfy the ever growing data trac demand in wireless communication systems, the current cellular systems have to be redesigned to increase both spectral eciency and energy eciency. Massive MIMO (Multiple-Input-Multiple-Output) is one solution that satisfy both requirements. In massive MIMO systems, hundreds of antennas are employed at the base station to provide service to many users at the same time and frequency. This enables the system to serve the users with uniformly good quality of service simultaneously, with low-cost hardware and without using extra bandwidth and energy. To achieve this, proper resource allocation is needed. Among the available resources, transmit power is one of the most important degree of freedom to control the spectral eciency and energy eciency. Due to the use of excessive number of antennas and low-end hardware at the base station, new aspects of power allocation compared to current systems arises. In the rst part of the thesis, a new uplink power allocation schemes that based on long term channel statistics is proposed. Since quality of the channel estimates is crucial in massive MIMO, in addition to data power allocation, joint power allocation that includes the pilot power as additional variable should be considered. Therefore a new framework for power allocation that matches practical systems is developed, as the methods developed in the literature cannot be applied directly to massive MIMO systems. Simulation results conrm the advantages brought by the the proposed new framework. In the second part of the thesis, we investigate the eects of using low-end ampliers at the base stations. The non-linear behavior of power consumption in these ampliers changes the power consumption model at the base station, thereby changes the power allocation. Two dierent scenarios are investigated and both results show that a certain number of antennas can be turned o in low load scenarios.
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| 4. |
- 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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| 5. |
- Ghazanfari, Amin, 1983-
(författare)
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Power Control for Multi-Cell Massive MIMO
- 2019
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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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| 6. |
- 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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| 7. |
- 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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| 8. |
- Mollén, Christopher, 1987-
(författare)
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On Massive MIMO Base Stations with Low-End Hardware
- 2016
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Massive MIMO (Multiple-Input Multiple-Output) base stations have proven, both in theory and in practice, to possess many of the qualities that future wireless communication systems will require. They can provide equally high data rates throughout their coverage area and can concurrently serve multiple low-end handsets without requiring wider spectrum, denser base station deployment or significantly more power than current base stations. The main challenge of massive MIMO is the immense hardware complexity and cost of the base station—each element in the large antenna array needs to be individually controllable and therefore requires its own radio chain. To make massive MIMO commercially viable, the base station has to be built from inexpensive simple hardware. In this thesis, it is investigated how the use of low-end power amplifiers and analog-to-digital converters (ADCs) affects the performance of massive MIMO. In the study of the signal distortion from low-end amplifiers, it is shown that in-band distortion is negligible in massive MIMO and that out-of-band radiation is the limiting factor that decides what power efficiency the amplifiers can be operated at. A precoder that produces transmit signals for the downlink with constant envelope in continuous time is presented to allow for highly power efficient low-end amplifiers. Further, it is found that the out-of-band radiation is isotropic when the channel is frequency selective and when multiple users are served; and that it can be beamformed when the channel is frequency flat and when few users are served. Since a massive MIMO base station radiates less power than today's base stations, isotropic out-of-band radiation means that low-end hardware with poorer linearity than required today can be used in massive MIMO. It is also shown that using one-bit ADCs—the simplest and least power-hungry ADCs—at the base station only degrades the signal-to-interference-and-noise ratio of the system by approximately 4 dB when proper power allocation among users is done, which indicates that massive MIMO is resistant against coarse quantization and that low-end ADCs can be used.
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| 9. |
- Moosavi, Reza
(författare)
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Improving the Efficiency of Control Signaling in Wireless Multiple Access Systems
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Prior to the transmission of payload data in any multiple access system, there is generally a need to send control information such as scheduling assignments, transmission parameters and HARQ acknowledgments. This process is called control signaling and has a significant impact on the overall system performance. This dissertation considers different aspects of control signaling and proposes some novel schemes for improving it. The dissertation is split into two parts where in the first part the focus is on the transmission of scheduling assignments, and in the second part the focus is on improving the “blind decoding” process that is used to achieve adaptive coding and modulation in transmission of control information.More specifically, in the first part of the dissertation we first compare the two conventional schemes for control signaling using extensive system simulations. In doing so, we use practical assumptions on the scheduling algorithm as well as on the compression and transmission of the scheduling information. We then provide two schemes for reducing the amount of control signaling that concerns the transmission of scheduling assignments. The first scheme, which is reminiscent of source coding with side information, uses the knowledge that each user has about its own channel condition to compress the scheduling information more effectively. The second scheme uses the fact that in wireless multiple access systems, a user with a given channel condition can in principle decode the data intended to the users that have weaker channels. Therefore, the idea is to send the scheduling information of different terminals in a differential manner starting from the user with the weakest channel and letting all the terminals overhear the transmission of one another. Finally, in the last section of this part we use some of the recent results in information theory to form a general framework for the comparison of different control signaling schemes. We formulate an optimization problem that for a given desired error probability finds the minimum required number of channel uses for a given signaling scheme.In the second part of the thesis, we propose three schemes for reducing the complexity of the blind decoding process. The first one is a novel scheme for fast blind identification of channel codes. More precisely, we propose an efficient algorithm that for a given sequence of received symbols and a given linear channel code, finds the posterior probability that all the parity check relations of the code aresatisfied. We then use this quantity to perform a sequential statistical hypotheses test that reduces the computational complexity of blind decoding. The idea in the second scheme is to broadcast a control message prior to the transmission of control information to instruct only a subset of the terminals (ideally only those terminals that have been scheduled for reception of payload data and hence benefit from performing a blind search attempt) to perform blind search decoding, which can be used for instance in LTE to reduce the complexity of the blind decoding process. Finally, in the third scheme we propose to split the CRC, used by the terminals to find their control information, into two parts and inject one part early in the control data stream so that the terminals can detect early if the current decoding attempt will be successful, which ultimately reduces the blind decoding complexity.
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| 10. |
- Pérez Herrera, Daniel, 1997-
(författare)
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Communication-Efficient Scheduling Designs for Distributed Consensus and Optimization over Wireless Networks
- 2024
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In recent years, there has been a significant surge in the development of artificial intelligence, with machine learning emerging as a fundamental aspect of its applications. Machine learning algorithms enable systems to learn from data and make predictions or decisions without explicit programming. In distributed environments, where data is often distributed across multiple nodes, decentralized learning methods have become increasingly prevalent. These methods allow for collaborative model training without using centralized data, offering benefits such as scalability, privacy, and efficiency. To ensure convergence and accuracy of the learned models, achieving consensus among distributed nodes is paramount. Consensus mechanisms enable nodes to agree on a common model despite variations in local data distributions and computational resources, forming the backbone of decentralized learning systems. Thus, the development of efficient consensus protocols is essential for realizing the potential of decentralized learning in various domains, ranging from IoT applications to large-scale data analytics.This thesis explores strategies to minimize the communication cost in wireless multi-agents systems. It examines the potential of leveraging the broadcast nature of wireless networks, focusing on two frameworks: distributed average consensus and decentralized learning.In distributed average consensus, wherein nodes aim to converge to the average of the initial values despite communication limitations, a novel probabilistic scheduling approach is proposed. This approach aims to streamline communication by selectively choosing a subset of nodes to broadcast information to their neighbors in each iteration. Various heuristic methods for determining node broadcast probabilities are evaluated, alongside the introduction of a pre-compensation technique to mitigate potential bias. These contributions shed light on the design of communication-efficient consensus protocols tailored to wireless environments with restricted resources.Transitioning to decentralized learning, the thesis introduces BASS (Broadcast-based Subgraph Sampling) to expedite the convergence of D-SGD (decentralized stochastic gradient descent) while considering the communication overhead. By generating a set of mixing matrix candidates that represent sparse subgraphs of the network topology, BASS facilitates the activation of collision-free subset of nodes in each iteration, optimizing communication efficiency. The optimization of sampling probabilities and the mixing matrices significantly enhances convergence speed and resource utilization compared to existing approaches. These findings underscore the inherent advantages of leveraging the broadcast capabilities of wireless channels to enhance the efficiency of decentralized optimization and learning algorithms in distributed systems.
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