Theses supervised by Prof. Dr. Tolga Mete Duman
21 theses · İhsan Doğramacı Bilkent University
Bellek içi analog hesaplama kullanarak kablosuz iletişim uygulamaları için öğrenme ve çıkarım
The exponential growth of wireless communication technologies has created a crucial need for more efficient and intelligent signal processing in decentralized devices and systems. Traditional digital computing architectures increasingly struggle to meet these rising computational demands, leading to performance bottlenecks and energy inefficiencies. The problem becomes more significant on edge devices with limited computing capabilities and severe energy limitations. Integrating machine learning algorithms with in-memory analog computing, specifically memristor-based architectures, provides a non-traditional computing paradigm and can potentially enhance the energy efficiency of edge devices. By leveraging the properties of memristors, which can perform both storage and computation, this research investigates ways to potentially reduce latency and power consumption in signal-processing tasks for wireless communications. This study examines memristor-based analog computing for deep learning and inference in three areas of (wireless) communications: cellular network traffic prediction, multi-sensor over-the-air inference for internet-of-things devices, and neural successive cancellation decoding for polar codes. The research includes the development of robust training techniques for memristive neural networks to cater for degraded performance due to noise in analog computations and offer acceptable prediction accuracy with reduced computational overhead for network traffic management. It explores in-memory computing for an L_p-norm inspired sensor fusion method with analog sensors and enables more efficient multi-sensor data fusion. Also, it investigates the incorporation of analog memristive computing in neural successive cancellation decoders for polar codes, which could lead to more energy-efficient decoding algorithms. The findings of the thesis suggest potential improvements in energy efficiency and provide insights into the benefits and limitations of using in-memory computing for wireless communication applications.
Ekleme ve silme kanallarında birleştirilmiş kodlar için derin öğrenme tabanlı kod çözücüler
Channels with synchronization errors, including insertion/deletion channels, are of significant importance, as they are encountered in various systems, such as communication networks and various storage technologies, including DNA data storage. Serially concatenated codes where the outer code is a powerful channel code, such as a low-density parity-check (LDPC) or convolutional code, and the inner code is a watermark or marker code, are shown to be effective solutions over such channels. In particular, the use of marker codes, referring to insertion of preselected sequences in the transmitted data stream periodically, are shown to work well in regaining synchronization at the receiver and achieving improved error rate performance compared to other alternatives. In the current literature, maximum a posteriori (MAP) detector realized by the well-known forward-backward algorithm is commonly employed to decode the inner marker code and estimate the log-likelihood ratios (LLRs) of the bits encoded by the outer code, and the resulting log-likelihood estimates are fed to the outer decoder to estimate the transmitted data. Alternative to the MAP detector, this thesis proposes deep learning-based solutions to estimate the LLRs of the coded bits in the paradigm of concatenated codes, exploiting the marker information and addressing some limitations of conventional methods. Bit-level deep learning-based detectors offer good alternatives when the channel statistics are not perfectly available at the decoder, degrading of the performance of the MAP detector. They can also be employed for one-shot decoding when the outer code is a convolutional code. Also developed are symbol-level deep learning-based detectors to exploit the correlations among adjacent bits at the detector output. Contrary to the existing symbol-level decoders for insertion/deletion channels, the newly proposed approaches can go beyond the case of combining three bits, offering further enhancements in performance while keeping the complexity tolerable. As a final contribution, deep learning-based detectors are developed for insertion and deletion channels that are further exacerbated by inter-symbol interference, e.g., modeling bit-patterned media recording channels, and their performance is studied via numerical examples.
Enerji hasadı ve heterojen veri dağılımı koşullarında kablosuz kanallarda havadan beslemeli federe öğrenme
With the growing interest in machine learning (ML), federated learning (FL) has emerged as a prominent paradigm for collaboratively training high-quality models across decentralized edge devices, while minimizing server-side data access and preserving user privacy. In FL, multiple mobile devices (MDs) are used to collaboratively train a global model with coordination from a parameter server (PS), with devices using their local data to perform stochastic gradient descent (SGD) for the purpose of global training. While extensive research has been conducted on FL over wireless channels and its applications in practical scenarios, the need to investigate and develop solutions tailored to highly heterogeneous setups is an open research direction. This thesis aims to explore strategies and propose solutions for over-the-air (OTA) FL over wireless channels in such heterogeneous environments. In the first part of this thesis, we focus on the scheduling strategies for the OTA FL with energy harvesting MDs and highly heterogeneous data distribution. We follow a paradigm to schedule users based on their data distribution characteristics and subsequently their transmitted updates, with the primary goal of improving the learning performance and making more efficient use of the limited harvested energy. We develop two scheduling approaches depending on whether the users' data distributions are known or unknown at the server. We provide a theoretical convergence analysis of the proposed OTA FL setup, which is also used to design scheduling strategies, and validate our findings through experimental analysis, demonstrating that scheduling strategies based on user characteristics can significantly enhance global learning performance and reduce redundancy in the system. In the second part of the thesis, we study clustered federated learning (CFL) approaches in OTA FL setup, focusing on personalized global models depending on users' data characteristics for energy harvesting MDs. In this part, we assume that users are naturally partitioned into distinct clusters; for instance, each cluster may consist of users interested in different categories of sports. The primary goal is to train a dedicated model for each cluster to better capture their specific preferences, while simultaneously serving all the clusters using a single parameter server through over-the-air transmission to ensure communication efficiency. To enable the simultaneous serving of each cluster, we propose different combining methods at the server side, each designed for a specific level of available channel state information (CSI). Numerical results show that with a sufficient number of receiver antennas, it is possible to support simultaneous update transmissions from multiple clusters and achieve more specialized global models with improved performance.
Enerji hasadı ve ortak enerji ve bilgi transferi için çalışma uzunluğu sınırlı kodları kullanarak kod dizaynı
Energy harvesting wireless networks and networks that benefit from wireless energy transfer have become popular in the last decade. In these networks, the users can obtain the required energy for transmission from an external source, which eliminates the need of battery replacement. Therefore, such networks have a high potential for applications in different areas including wireless sensor networks, wireless body networks and Internet of Things (IoT). While there have been many advancements for energy harvesting communications and joint energy and information transfer from information and communication theoretic perspectives in the literature, these subjects have not been studied from a practical coding and transmission point of view in depth. With the above motivation, in this thesis, we propose a serially concatenated coding scheme to communicate over binary energy harvesting communication channels with additive white Gaussian noise (AWGN), and design explicit and implementable codes for both long and short block lengths. Run length limited (RLL) codes are used to induce the required nonuniform input distributions for both cases. We employ low density parity check (LDPC) codes for long block lengths, while for short block length designs, we utilize convolutional codes for error correction. We consider different decoding approaches for the two cases, i.e., an iterative decoder is used for the former while Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm over the product trellis of the convolutional and run length limited codes is used for the latter. Also, by noticing that similar coding solutions can be employed, we extend our work to joint energy and information transfer for both scenarios. Numerical examples demonstrate that the newly optimized codes with an inner RLL code are superior to the point-to-point optimal codes for AWGN channels for long block lengths when energy harvesting or joint energy and information transfer is considered, and that, for the short block length case, concatenated convolutional and RLL codes with higher minimum distances offer excellent performance.
Girişim kanalları için kod tasarımı
As the number of wireless devices dramatically increases, they experience more interference in their communications. As a result, managing interference in wireless networks is an important challenge in future wireless communication systems, which can be tackled in different layers of communications. Designing good channel codes, which can enable reliable communication close to the information theoretic limits in the presence of interference, is one of the ways to increase the quality of service. With the above motivation, in this research, we focus on code design for interference channels (ICs). We, first consider classical two-user fading IC and study implementation of different encoding/decoding schemes with low-density parity-check (LDPC) codes for both quasi-static and fast fading scenarios. We adopt the Han-Kobayashi (HK) type encoding, derive stability conditions on the degree distributions of LDPC code ensembles, and obtain explicit and practical code designs. In order to estimate the decoding thresholds, a modified form of the extrinsic information transfer (EXIT) chart analysis based on binary erasure channel (BEC) approximation for the incoming messages from the component LDPC decoders to state nodes is developed. The proposed code design is employed in several examples for both fast and quasi-static fading cases. A comprehensive set of examples demonstrates that the designed codes perform close to the achievable information theoretic limits. Furthermore, multiple antenna transmissions employing the Alamouti scheme for fading ICs are studied; a special receiver structure is developed, and specific codes are explored. Finally, advantages of the designed codes over point-to-point (P2P) optimal ones are demonstrated via both asymptotic and finite block length simulations. Next, we consider cognitive interference channels (CICs), a variant of classical two-user ICs in which one of the transmitters (cognitive transmitter) has non-causal knowledge of the other's (private user's) message. Prompted by the information theoretical results, we design an explicit coding scheme for CIC in the primary decodes cognitive regime. We present a novel joint decoder and design LDPC codes for our set-up. Simulation results demonstrate that the proposed joint decoder and the designed codes outperform the conventional maximum ratio combining type decoder and the point-to-point optimal codes, respectively. Later, we propose and evaluate the idea of flexible modulation for P2P communication with available channel side information at the transmitter. This technique does not perform as well as dirty paper coding (DPC); however, its simplicity is a major advantage. Also the flexible modulation technique shows more robustness to inaccuracy in the channel state information. Finally, we consider a multiple access channel (MAC) in which the non-causal knowledge of one of the users' message is available at the other user. We consider both Gaussian channel and fading scenarios. We propose the idea of joint encoding, and study its performance via simulations demonstrating that the proposed approach outperforms the classical coding scheme.
Kablosuz kanallar üzerinde dağıtık önbelleğe alma ve makine öğrenmesi
Coded caching and coded computing have drawn significant attention in recent years due to their advantages in reducing the traffic load and in distributing computational burden to edge devices. There have been many research results addressing different aspects of these problems; however, there are still various challenges that need to be addressed. In particular, their use over wireless channels is not fully understood. With this motivation, this thesis considers these two distributed systems over wireless channels taking into account realistic channel effects as well as practical implementation constraints. In the first part of the thesis, we study coded caching over a wireless packet erasure channel where each receiver encounters packet erasures independently with the same probability. We propose two different schemes for packet erasure channels: sending the same message (SSM) and a greedy approach. Also, a simplified version of the greedy algorithm called the grouped greedy algorithm is proposed to reduce the system complexity. For the grouped greedy algorithm, an upper bound for transmission rate is derived, and it is shown that this upper bound is very close to the simulation results for small packet erasure probabilities. We then study coded caching over non-ergodic fading channels. As the multicast capacity of a broadcast channel is restricted by the user experiencing the worst channel conditions, we formulate an optimization problem to minimize the transmission time by grouping users based on their channel conditions, and transmit coded messages according to the worst channel in the group, as opposed to the worst among all. We develop two algorithms to determine the user groups: a locally optimal iterative algorithm and a numerically more efficient solution through a shortest path problem. In the second part of the thesis, we study collaborative machine learning (ML) systems, which is also known as federated learning, where a massive dataset is distributed across independent workers that compute their local gradient estimates based on their own datasets. Workers send their estimates through a multipath fading multiple access channel (MAC) with orthogonal frequency division multiplexing (OFDM) to mitigate the frequency selectivity of the channel. We assume that the parameter server (PS) employs multiple antennas to align the received signals with no channel state information (CSI) at the workers. To reduce the power consumption and hardware costs, we employ complex-valued low-resolution analog to digital converters (ADCs) at the receiver side and study the effects of practical low cost ADCs on the learning performance of the system. Our theoretical analysis shows that the impairments caused by a low-resolution ADC do not prevent the convergence of the learning algorithm, and fading effects vanish when a sufficient number of antennas are used at the PS. We also validate our theoretical results via simulations, and further, we show that using one-bit ADCs causes only a slight decrease in the learning accuracy.
Gelişmiş asenkron rastgele erişim protokolleri
Fifth generation wireless systems and beyond require linking an enormous number of simple machine type devices leading to a new wave of interest in massive machine type communications (mMTC). Different from the human-centric communication systems, mMTCs are composed of a large number of devices where each user node generates small data blocks sporadically in an unpredictable manner. In such scenarios, traditional multiple access schemes, e.g., time division multiple access or frequency division multiple access, are not suitable because resource allocation and scheduling based approaches cannot be conveniently adopted due to the required complexity and latency, motivating the use of uncoordinated random access (RA) protocols and making asynchronous ALOHA-like solutions ideal candidates for such applications. In this thesis, we consider the design and analysis of advanced asynchronous RA protocols for different settings. We first study contention resolution ALOHA (CRA) and irregular repetition ALOHA (IRA) protocols with regular and irregular repetition rates on the collision channel where collisions are resolved through successive interference cancellation. We also propose concatenation of packet replicas with some clean parts with IRA, named irregular repetition ALOHA with replica concatenation (IRARC). Secondly, we introduce energy harvesting (EH) into the framework with the motivation of self-sustainability, and study RA protocols with EH nodes. Finally, we propose a generalization of IRA with packet length diversity to improve the system performance further. We present asymptotic analyses of all the proposed RA protocols, and determine the optimal repetition distributions to maximize the system throughput. We also provide a comprehensive set of numerical results for both asymptotic and practical scenarios to further demonstrate the effectiveness of the proposed approaches.
Çoklu giriş çoklu çıkış sistemlerde semboller arası girişim kanalları için derin öğrenme tabanlı kanal eşitleme
Future wireless communications is expected to bring significant changes along with a number of emerging technologies such as 5G, virtual reality, edge computing, and IoT. These developments pose unprecedented demands in terms of capacity, coverage, latency, efficiency, flexibility, compatibility, and quality of experience on wireless communication systems. Machine Learning (ML) techniques are considered as a promising tool to tackle this challenge due to their ability to manage big data, powerful nonlinear mapping, and distributed computing capabilities. There have been many research results addressing different aspects of ML algorithms and their connections to wireless communications; however, there are still various challenges that need to be addressed. In particular, their use for communication systems with memory, is not fully investigated. With this motivation, this thesis considers an application of ML, in particular, deep learning (DL), techniques for communications over intersymbol interference (ISI) channels. In this thesis, we propose DL-based channel equalization algorithms for channels with ISI. We introduce three different DL-based ISI detectors, namely sliding bidirectional long short term memory (Sli-BiLSTM), sliding multi layer perceptron (Sli-MLP), and sliding iterative (Sli-Iterative), and demonstrate that they are computationally efficient and capable of performing equalization under a variety of channel conditions with the knowledge of the channel state information. We also employ sliding bidirectional gated recurrent unit (Sli-BiGRU) and Sli-MLP, which are more suitable for use with fixed ISI channels. As an extension, we also examine DL-based equalization techniques for multiple-input multiple-output (MIMO) ISI channels. Numerical results show that proposed models are well suited for equalization of ISI channels with perfect as well as noisy CSI for a broad range of signal-to-noise ratio (SNR) levels as long as the ISI length is not excessive. It is also shown that the proposed DL-based ISI detectors perform very close to the optimal solution, namely, the maximum likelihood sequence estimation, implemented through the Viterbi Algorithm while having considerably less complexity, and they have superior performance compared to MMSE-based channel equalization.
Haberleşme sistemleri için derin öğrenme: Yeni kanal kestirimi, denkleştirme ve güvenli iletim çözümleri
Traditional communication system design takes a model-based approach that aims to optimize relevant performance metrics using somewhat simple and tractable channel and signal models. For instance, channel codes are designed for simple additive white Gaussian or fading channel models, channel equalization algorithms are based on mathematical models for inter-symbol interference (ISI), and channel estimation techniques are developed with the underlying channel statistics and characterizations in mind. Through utilizing superior mathematical models and expert knowledge in signal processing and information theory, the model-based approach has been highly successful and has enabled development of many communication systems until now. On the other hand, beyond 5G wireless communication systems will further exploit the massive number of antennas, higher bandwidths, and more advanced multiple access technologies. As communication systems become more and more complicated, it is becoming increasingly important to go beyond the limits of the model-based approach. Noting that there have been tremendous advancements in learning from data over the past decades, a major research question is whether machine learning based approaches can be used to develop new communication technologies. With the above motivation, this thesis deals with the development of deep neural network (DNN) solutions to address various challenges in wireless communications. We first consider orthogonal frequency division multiplexing (OFDM) over rapidly time-varying multipath channels, for which the performance of standard channel estimation and equalization techniques degrades dramatically due to inter-carrier interference (ICI). We focus on improving the overall system performance by designing DNN architectures for both channel estimation and data demodulation. In addition, we study OFDM over frequency-selective channels without cyclic prefix insertion in an effort to improve the overall throughputs. Specifically, we design a recurrent neural network to mitigate the effects of ISI and ICI for improved symbol detection. Furthermore, we explore secure transmission over multi-input multi-output multi-antenna eavesdropper wiretap channels with finite alphabet inputs. We use a linear precoder to maximize the secrecy rate, which benefits from the generalized singular value decomposition to obtain independent streams and exploits function approximation abilities of DNNs for solving the required power allocation problem. We also propose a DNN technique to jointly optimize the data precoder and the power allocation for artificial noise. We use extensive numerical examples and computational complexity analyses to demonstrate the effectiveness of the proposed solutions.
Kablosuz kanallar üzerinde federe öğrenme ve dağıtık çıkarım
In an era marked by massive connectivity and a growing number of connected devices, we have gained unprecedented access to a wealth of information, enhancing the reliability and precision of intelligent systems and enabling the development of learning algorithms that are more capable than ever. However, this proliferation of data also introduces new challenges for centralized learning algorithms for the training and inference processes of these intelligent systems due to increased traffic loads and the necessity of substantial computational resources. Consequently, the introduction of federated learning (FL) and distributed inference systems has become essential. Both FL and distributed inference necessitate communication within the network, specifically, the transmission of model updates and intermediate features. This has led to a significant emphasis on their utilization over wireless channels, underscoring the pivotal role of wireless communications in this context. In pursuit of a practical implementation of federated learning over wireless fading channels, we direct our focus towards cost-effective solutions, accounting for hardware-induced distortions. We consider a blind transmitter scenario, wherein distributed workers operate without access to channel state information (CSI). Meanwhile, the parameter server (PS) employs multiple antennas to align received signals. To mitigate the increased power consumption and hardware cost, we leverage complex-valued, low-resolution digital-to-analog converters (DACs) at the transmitter and analog-to-digital converters (ADCs) at the PS. Through a combination of theoretical analysis and numerical demonstrations, we establish that federated learning systems can effectively operate over fading channels, even in the presence of low-resolution ADCs and DACs. As another aspect of practical implementation, we investigate federated learning with over-the-air aggregation over time-varying wireless channels. In this scenario, workers transmit their local gradients over channels that undergo time variations, stemming from factors such as worker or PS mobility and other transmission medium fluctuations. These channel variations introduce inter-carrier interference (ICI), which can notably degrade the system performance, particularly in cases of rapidly varying channels. We examine the effects of the channel time variations on FL with over-the-air aggregation, and show that the resulting undesired interference terms have only limited destructive effects, which do not prevent the convergence of the distributed learning algorithm. Focusing on the distributed inference concept, we also consider a multi-sensor wireless inference system. In this configuration, several sensors with constrained computational capacities observe common phenomena and engage in collaborative inference efforts alongside a central device. Given the inherent limitations on the computational capabilities of the sensors, the features extracted from the front part of the network are transmitted to an edge device, which necessitates sensor fusion for the intermediate features. We propose $L_p$-norm inspired and LogSumExp approximations for the maximum operation as a sensor fusion method, resulting in the acquisition of transformation-invariant features that also enable bandwidth-efficient feature transmission. As a further enhancement of the proposed method, we introduce a learnable sensor fusion technique inspired by the $L_p$-norm. This technique incorporates a trainable parameter, providing the flexibility to customize the sensor fusion according to the unique network and sensor distribution characteristics. We show that by encompassing a spectrum of behaviors, this approach enhances the adaptability of the system and contributes to its overall performance improvement.
Kablosuz kanallar üzerinde düşük karmaşıklıklı kaynaksız rastgele erişim şemalarının tasarımı
The Sixth Generation and Beyond communication systems are expected to enable communications of a massive number of machine-type devices. The traffic generated by some of these devices will significantly deviate from those in conventional communication scenarios. For instance, for applications where a massive number of cheap sensors communicate with a base station (BS), the devices will only be sporadically active and there will be no coordination among them or with the BS. For such systems requiring massive random access solutions, a new paradigm called unsourced random access (URA) has recently been proposed. In URA, all the users employ the same codebook and there is no user identity. The destination is only interested in the list of messages being sent from the set of active users. While there are many interesting URA schemes developed in the recent literature, many significant challenges remain, in particular in designing low-complexity and energy-efficient solutions. With the motivation of addressing the current challenges in URA, we develop practical solutions for several scenarios. First, we propose and study URA over frequency-selective channels via orthogonal frequency division multiplexing to mitigate the fading effects. The decoder employs a joint activity detection and channel estimation algorithm coupled with treating interference as noise and successive interference cancellation (SIC). Our results show that the proposed scheme offers competitive performance with grant-based frequency division multiple-access while the performance loss due to the estimated channel state information is limited. We then examine the scenario for which the receiver is equipped with a massive number of antennas and develop a simple yet energy-efficient solution by dividing the transmission frame into slots where each active user utilizes a non-orthogonal pilot sequence followed by its polar encoded codeword. At the receiver, we first detect the transmitted pilot sequences by a generalized orthogonal matching pursuit algorithm and utilize a linear minimum mean square error (LMMSE) solution to estimate the channel vectors. We then perform iterative decoding based on maximal ratio combining and single-user decoding followed by SIC. Numerical examples and analysis results demonstrate that the proposed scheme either outperforms the existing approaches in the literature or has a competitive performance with lower complexity. We then adapt our solution to the scenarios with residual hardware impairments (HWIs) at the BS and the user equipment sides by developing a hardware-impairment aware LMMSE solution for channel estimation using the HWI statistics and observe that the newly proposed solution improves the energy efficiency and increases the number of supported active users. Finally, we study on-off division multiple access in the context of URA where each active user utilizes a small fraction of the transmission frame and show that the new approach is superior to the existing ones in terms of performance or complexity.
Frekans seçici azalan kanallarda fiziksel katmanda güvenlik
The inherent open nature of the transmission medium makes security a challenging issue in wireless networks. Physical layer security, which is an alternative or a complement to the cryptographic approaches, exploits the differences between the physical properties of different channels in order to provide secrecy. The idea is to ensure that the received signal at an eavesdropper is degraded compared to that of the legitimate receiver in some sense which guarantees that the confidential messages cannot be recovered by an unintended receiver. Over the last decade, various researchers have studied fundamental limits of physical layer security under different wiretap channel models, including Gaussian and fading channels, and with different assumptions on the transmitter's knowledge on the channel state information. In this thesis, we study physical layer security over frequency selective fading channels modelling certain wireless links. Specifically, we investigate optimal and suboptimal power allocation schemes across frequencies with perfect and partial channel state information at the transmitter with the objective of providing secrecy. We demonstrate that frequency selectivity allows for positive secrecy rates even though the eavesdropper's channel is not a degraded version of the desired user's channel. We also analyse the impact of user mobility and the resulting time variations in the wireless medium on the achievable secrecy rates. Furthermore, we consider quantized channel state information at the transmitter and evaluate the secrecy rate loss due to limited feedback from the legitimate receiver to the transmitter. Our results reveal that the partial channel state information at the transmitter can still be helpful in providing positive secrecy rates.
Sonlu girdi setine sahip simgeler arası girişim kanallarındaki güvenlik seviyesi
Due to the broadcast nature of the communication medium, security is a critical problem in wireless networks. Securing the transmission at the physical layer is a promising alternative or complement to the conventional higher level techniques such as encryption. During the past decade, various studies have been carried out which investigate such possibilities in providing secrecy for different scenarios. On the other hand, secrecy over intersymbol interference (ISI) channels has not received significant attention, and much work remains to be done. With this motivation, we focus on secrecy rates of finite-input ISI channels for both fixed and fading channel coefficients. We argue that the secrecy rates of ISI channels can be computed by the forward recursion of the BCJR algorithm. Moreover, by utilizing Markov input distributions for transmission over the ISI channels, achievable secrecy rates can be increased. However, the existing iterative method in the literature to obtain the optimal Markov input distribution is computationally complex as many BCJR recursions are needed. Thus, we propose an alternative solution by introducing a codebook based approach. Particularly, among the existing Markov input distributions in the codebook, we propose to select the one which spectrally matches the main channel. Our numerical results reveal that the proposed low complexity approach undergoes a minimal loss with respect to the existing iterative algorithm while offering a considerably reduced complexity. We also propose injection of artificial noise (AN) to increase the secrecy rates, and show that this is especially useful for moderate and high signal to noise ratio (SNR) values where the use of Markov input distributions is not beneficial. We inject AN to frequencies where the eavesdropper's channel is better than the main channel. We show that this approach significantly increases the secrecy rates compared to the existing methods. Furthermore, we consider the effect of channel state information (CSI) on the secrecy rates, and demonstrate that availability of eavesdropper's CSI at the transmitter is highly beneficial in terms of the achievable secrecy rates.
Hat dinlemeli kanallar için rasgeleleştirilmiş kıvrımlı ve uç uça eklemelı kodlar
Wireless networks are vulnerable to various kinds of attacks such as eavesdropping because of their open nature. As a result, security is one of the most important challenges that needs to be addressed for such networks. To address this issue, we utilize information theoretic secrecy approach and develop randomized channel coding techniques akin to the approach proposed by Wyner as a general method for confusing the eavesdropper while making sure that the legitimate receiver is able to recover the transmitted message. We first study the application of convolutional codes to the randomized encoding scheme. We argue how dual of a code plays a major role in this construction and obtain dual of a convolutional code in a systematic manner. We propose optimal and sub-optimal decoders for additive white Gaussian noise (AWGN) and binary symmetric channels and obtain bounds on the decoder performance extending the existing lower and upper bounds on the error rates of coded systems with maximum likelihood (ML) decoding. Furthermore, we apply list decoding to improve the performance of the sub-optimal decoders. We demonstrate via several examples that security gaps achieved by the randomized convolutional codes compete favorably with some of the existing coding methods. In order to improve the security gap hence the system performance further, we develop concatenated coding approaches applied to the randomized encoding scheme as well. These include serial and parallel concatenated convolutional codes and serial concatenation of a low density generator matrix code with a convolutional code. For all of these solutions low-complexity iterative decoders are proposed and their performance in the wiretap channel is evaluated in terms of the security gap. Numerical examples show that for certain levels of confusion at the eavesdropper, randomized serially concatenated convolutional codes offer the best performance.
Büyük çok girdili çok çıktılı sistemlerde çok genlikli ön kodlama
Wireless communications is an important part of information and communication technologies. Particularly, with the introduction of 5G wireless systems, higher data rates, ultra-low latencies and improved power e ciencies are demanded. It is understood that multiple-input multiple-output (MIMO) systems constitute some of the promising technologies to meet these demands, however, currently used number of antennas at the base stations (BS) is not su cient to reveal the full potential. As a result, massive MIMO systems which use a very large number of antennas at the BSs have recently been proposed as enabling solutions. While massive MIMO promises much for 5G and beyond wireless technologies, there are many problems to be solved including lowering of high built-in and operating costs of BSs to make this technology practical. Constant envelope (CE) precoding has recently been proposed as a way to reduce the hardware complexity of massive MIMO systems. CE precoding technique for downlink enables a BS structure with one (nonlinear) power ampli er (PA) coupled with continuous or discrete phase shifters in front of each antenna instead of separate highly linear PAs driving each. While CE precoding o ers signi cant reductions in hardware costs, it results in some performance loss in terms of achievable data rates and power e ciencies compared to conventional zero forcing precoding based approaches. In this thesis, we build on the CE precoding idea and propose the use of a multienvelope precoding technique for massive MIMO systems which utilizes more than one (but only a few, e.g., 2 or 3) PAs with the objective of recovering some of the performance loss due to the use of CE precoding. The proposed multi-envelope precoding method relies on the standard zero forcing algorithm to group the antennas, and then it utilizes an envelope with a higher level on the antenna group(s) requiring higher power. In other words, the number of power levels used equals to the number of antenna groups. We explore the use of both continuous and discrete phase shifters, and via extensive simulations, we demonstrate that the newly proposed approaches provide signi cant performance improvements over the CE solutions closing some of the performance gap with average power constraint precoding.
Enerji hasatlayan düğümlerle düzensiz tekrarlı dilimli aloha
The importance of wireless networking schemes originating from ALOHA has rapidly risen with the wide-spread use of Internet, advancements in the communications systems and increasing number of wireless devices. Internet-of-Things and machine-to-machine communications concepts have drawn further attention to ALOHA since it is a low-complexity protocol. However, the classical ALOHA is not efficient and cannot handle massive number of users in an efficient manner. Therefore, many improvements have been proposed for over the years. Irregular Repetition Slotted ALOHA (IRSA) is an advanced ALOHA protocol in which each user sends a variable number of copies of their packets in each fixed length medium access control (MAC) frame. The collisions may be resolved via successive interference cancellation (SIC) using the copies that are received cleanly. In this way, asymptotic throughputs close to the maximum normalized throughput value of one on the collision channel may be achieved. In this thesis, to reap the benefits of IRSA for energy harvesting sensor networks, we propose an IRSA based uncoordinated random access scheme for energy harvesting (EH) nodes. Specifically, we consider the case in which each user has a finite-sized battery which is recharged in a probabilistic manner in each slot with harvested energy from the environment. We analyze this scheme by deriving asymptotic throughput expressions, and obtain optimized probability distributions for the number of packet replicas for each user. We demonstrate that the optimized distributions perform considerably better than those of slotted ALOHA (SA), contention resolution diversity slotted ALOHA (CRDSA) and plain IRSA which do not take into account EH for both asymptotic and finite frame length scenarios.
Kablosuz bağlantılar üzerinden rastgele erişim: En uygun kodlama oranı ve aktivite olasılığı seçimi
Due to the rapidly increasing number of devices in wireless networks with the proliferation of applications based on new technologies such as machine to machine communications and Internet of Things, there is a growing interest in the random access schemes as they provide a simple means of channel access. To this end, various schemes have been proposed based on the ALOHA protocol to increase the efficiency of the medium access control layer over the last decade. On the other hand, physical layer aspects of random access networks have received relatively limited attention, and there is a need to consider optimal use of the underlying physical layer properties especially for transmission over wireless channels. In this thesis, we study uncoordinated random access schemes over wireless fading channels where each user independently decides whether to send a packet or not to a common receiver at any given time slot. To characterize the system throughput, i.e., the expected sum-rate, an information theoretic formulation is developed. We consider two scenarios: classical slotted ALOHA, where no multi-user detection (MUD) capability is available and slotted ALOHA with MUD. Our main contribution is that the optimal rates and the channel activity probabilities can be characterized as a function of the user distances to the receiver to maximize the system throughput in each case (more precisely, as a function of the average signal to noise ratios of the users). We use Rayleigh fading as our main channel model, however, we also study the cases where log-normal shadowing is observed along with small scale fading. Our proposed optimal rate selection schemes offer significant increase in expected system throughput compared to the same rate approach commonly used in the literature. In addition to the overall throughput optimization, the issue of fairness among users is also investigated and solutions which guarantee a minimum amount of individual throughput are developed. We also design systems with limited individual outage probabilities of the users for increased energy efficiency and reduced delay. All of these analytical works are supported with detailed numerical examples, and the performance of the proposed methods are evaluated.
Enerji hasadı yapılan ve çok-kullanıcılı haberleşme sistemleri için kodlama yöntemleri
Many wireless communication and networking applications can benefit from energy harvesting and wireless energy transfer including wireless sensor networks, radio frequency identification systems and wireless body networks. Some of the advantages that energy harvesting provides for such applications include energy self-sufficiency, ability to implement them in hard-to-reach places, reducing the required battery size or even removing the battery completely from the wireless units. In such systems the required energy for the system operation is obtained from a renewable energy source such as solar, thermal or kinetic energy or from a man-made source such as radio frequency (RF) signals, artificial light, etc. While there has been decades of designs and developments of energy harvesting nodes from circuit and device engineering perspectives, only recent studies consider the specific constraints of these systems from a communications point of view, and significant challenges and problems still remain unsolved, particularly, at the physical layer. With the motivation of addressing some of the above challenges, our main focus in this thesis is the design and analysis of capacity approaching coding schemes for several energy harvesting and multiuser scenarios; in particular, by exploiting nonlinear codes concatenated with low-density parity-check (LDPC) codes for these scenarios. First, novel code design approaches are studied for the joint energy and information transfer specifically, employment of nonlinear trellis codes (NLTCs) in serial concatenation with outer LDPC codes is proposed, and an algorithm is developed to design the NLTCs prior to optimizing the outer LDPC code using the EXIT analysis. The designed codes are shown to improve upon the off-the-shelf point-to-point (P2P) codes and outperform the alternative of utilizing linear codes with time switching and the reference scheme of concatenating LDPC codes with nonlinear memoryless mappers (NLMMs). This coding approach is then examined for the energy harvesting channel (EHC) implementing two decoding approaches at the receiver side wherein the first one ignores the memory in the battery state, while the second one incorporates this memory into the trellis. Compared with the P2P codes and the reference schemes, the newly designed codes consistently offer better performance. This code design approach is explored for the case of discrete memoryless interference channels (DMICs) implementing the Han-Kobayashi (HK) encoding and decoding strategy as well. A stability condition is derived for the concatenated coding scheme and it is utilized in the process of designing the outer LDPC code employing the EXIT analysis. It is demonstrated that the designed codes achieve rate pairs close to the optimal boundary of the HK subregion and outperform the single user codes with time sharing. Furthermore, code design principles are also investigated for the two-user Gaussian interference channel with fading employing trellis-based codes with short block lengths. Finally, the problem of designing explicit and implementable codes is studied for a two-user interference channel with energy harvesting transmitters, and a design framework is proposed employing similar techniques developed for the DMIC and EHC.
Sonlu girdi setine sahip sinyallerle çok antenli güvenli iletim
With the ever-growing demand for services that rely on transmission over wireless networks, a challenging issue is the security of the transmitted information. Due to its open nature, wireless communications is prone to eavesdropping attacks. Typically, secrecy of the transmitted information is ensured with the aid of cryptographic techniques, which are deployed on upper layers of the network protocol stack. However, due to the need for key distribution and management, cryptographic solutions are difficult to implement in decentralized networks. Moreover, the security provided by key based solutions is not provable from a mathematical point of view. Physical layer security is an alternative or complement to the cryptographic techniques, which can resolve the complexities associated with key distribution and management. The basic principle of physical layer security is to exploit the randomness of the communication channels to allow a transmitter deliver its message to an intended receiver reliably while guaranteeing that a third party cannot infer any information about it. Much of the existing research in physical layer security focuses on investigating the information theoretic limits of secure communications. Among different techniques proposed, multiple-antenna based solutions have been shown to exhibit a high potential for enhancing security. Furthermore, Gaussian inputs are proved to be the optimal input distributions in a variety of scenarios. However, due to the high detection complexity, Gaussian signaling is not used in practice, and the transmission is carried out with the aid of symbols drawn from standard signal constellations. In this thesis, we develop several secure multi-antenna transmission techniques under the practical finite-alphabet input assumption. We first consider multiple-input multiple-output (MIMO) wiretap channels under finite-alphabet input constraints. We assume that the statistical channel state information (CSI) of the eavesdropper is available at the transmitter, and study two different scenarios regarding the transmitter's knowledge on the main channel CSI (MCSI) including availability of perfect and statistical MCSI at the transmitter. In each scenario, we introduce iterative algorithms for joint optimization of data precoder and artificial noise. We also propose different strategies to reduce the computational complexity associated with the transmit signal design. Moreover, we consider the setups with simultaneous wireless information and power transfer (SWIPT), and propose transmission schemes for achieving the trade-off between the secrecy rate and the harvested power. We demonstrate the efficacy of the proposed transmit signal design algorithms via extensive numerical examples. We also introduce several secure transmission schemes with spatial modulation and space shift keying (SSK). We derive an expression for the achievable secrecy rate, and develop precoder optimization algorithms for its maximization using transmitter side CSI. Furthermore, we introduce a group of secure SSK transmission schemes, which rely on dynamic antenna index assignment over reciprocal channels. Our results reveal that the fundamentally different working principle of SSK opens up new avenues for secure multi-antenna transmission.
Sönümlü ve eklemeli beyaz Gauss gürültü kanalları üzerinde polarizasyon ayarlı evrişimli kodlar
Ultra-reliable and low-latency communications (URLLC), which focuses on delay sensitive applications and services, is one of the three main pillars of 5G New Radio (NR) network architecture. URLLC's physical layer design is challenging since it must meet two contradictory requirements: ultra-low latency and ultra-high reliability. Short packets are used to minimize latency but at the cost of a significant loss of coding gain. Alternatively, system bandwidth can be increased, which is not always practical, particularly for some URLLC applications in industrial control that use unlicensed spectrum. In order to improve reliability, we must utilize robust channel codes in conjunction with retransmission techniques. Therefore, the construction of block codes with short blocklengths (e.g., a thousand or less information bits) is receiving significant attention with emerging wireless communications applications. In this thesis, we review existing channel coding bounds with short blocklengths for both additive white Gaussian noise (AWGN) and block fading channels. Furthermore, we investigate the performances of tail-biting convolutional, polar, and polarization adjusted convolutional (PAC) codes. With the motivation of reducing the decoding complexity of PAC decoders, we implement an alternative sequential decoding algorithm, namely, creeper algorithm, and describe a simplified list decoding approach. We also conduct an investigation on the performance of PAC codes and channel coding limits for block fading channels. Furthermore, we derive a method for computing approximate weight distribution of PAC codes, which can be used for an accurate performance bound; and, employing this approximation, we design PAC codes utilizing simulated annealing for optimization of the rate profiles. The results show that the newly designed PAC code rate profiles offer superior performance.
Novel unsourced random access algorıthms over gaussıan and fadıng channels
Random access techniques play a crucial role in machine-type communications (MTC), especially in the context of massive and sporadic device connectivity. Unlike traditional communication systems with scheduled access, random access allows devices to independently access the network without prior coordination. This flexibility is particularly beneficial for MTC scenarios where a large number of devices may transmit data sporadically. Unsourced random access (URA) is a form of grant-free random access in which devices remain entirely unidentified. As a result, there is no need for a codebook to store device identity preambles, whose dimension is squared to the number of connected users. This elimination of the codebook requirement empowers URA to efficiently accommodate an unbounded number of devices, reaching hundreds of millions of devices. This thesis proposes three unsourced random access algorithms suitable for Gaussian and wireless fading channels. First, we introduce a URA algorithm for use over Gaussian multiple access channels. In the proposed solution, the users are randomly separated by assigning varying levels of transmit power to each of them. This introduces power diversity, enhancing the system performance. In the second part, we offer a solution for URA over Rayleigh block-fading channels with a receiver equipped with multiple antennas. We employ a slotted structure with multiple stages of orthogonal pilots; each randomly picked from a codebook. In the proposed signaling structure, each user encodes its message using a polar code and appends it to the selected pilot sequences to construct its transmitted signal. The receiver employs an iterative algorithm to detect messages transmitted by different users. This algorithm comprises several components, including pilot detection, channel estimation, soft data detection, single-user polar decoder, and successive interference cancellation. Additionally, we improve this scheme by incorporating an efficient strategy that separates users by random grouping. Our extensive analytical and simulation results demonstrate the effectiveness of the proposed algorithm in terms of both energy efficiency and computational complexity. In the last part of the thesis, we study URA employing a passive reconfigurable intelligent surface, facilitating connections between the users and the base station when the direct link is blocked or significantly attenuated. We demonstrate through extensive simulations and analytical results that the proposed approach notably enhances system performance, particularly in channels with significant attenuation.