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Channel estimation is a fundamental task for exploiting the advantages of massive Multiple-Input Multiple-Output (MIMO) systems in fifth generation (5G) wireless technology. Channel estimates require solving sparse linear inverse problems that is usually performed with the Least Squares method, which brings low complexity but high mean squared error values. Thus other methods are usually needed to obtain better results, on top of Least Squares. Approximate Message Passing (AMP) is an efficient method for solving sparse linear inverse problems and recently a deep neural network approach to quickly solving such problems has been proposed, called Learned Approximate Message Passing (LAMP) [1], which estimates AMP with a fixed number iterations and learnable parameters. We formalize the channel estimation problem as a dictionary-based sparse linear inverse problem and investigate the applicability of LAMP to the task. We build upon the work of Borgerding et al. [1], providing a new loss function to minimize for our dictionary-based problem, we investigate empirically LAMP’s capabilities in various conditions: varying the dataset size, number of subcarriers, depth of network, and signal-to-noise ratio (SNR). We also propose a new network called Adaptive-LAMP which differs from LAMP for the introduction of a small neural network in each layer for estimating certain parameters instead of learning them. Experiments show that LAMP performs significantly better than AMP in terms of NMSE at low signal-to-noise ratio (SNR) levels and worse at high SNR levels. Interestingly, both proposed networks perform well at discovering active paths in cellular networks, paving the way for new approaches to the Channel Estimation problem. / Kanalbedömning är en grundläggande uppgift för att utnyttja fördelarna med massiva MIMO-system (Multiple-Input Multiple-Output) i femte generationens (5G) trådlösa teknik. Kanalskattningar kräver att man löser glesa linjära inversa problem som vanligtvis utförs med Least Squares-metoden, som ger låg komplexitet men höga medelvärden för det kvadratiska felet. Därför behövs vanligtvis andra metoder för att få bättre resultat, utöver Least Squares. Approximate Message Passing (AMP) är en effektiv metod för att lösa sparsamma linjära inversa problem, och nyligen har det föreslagits ett djupt neuralt nätverk för att snabbt lösa sådana problem, kallat Learned Approximate Message Passing (LAMP) [1], som uppskattar AMP med ett fast antal iterationer och inlärningsbara parametrar. Vi formaliserar kanalskattningsproblemet som ett ordboksbaserat sparse linjärt inversproblem och undersöker LAMP:s tillämplighet på uppgiften. Vi bygger på Borgerding et al. [1], som tillhandahåller en ny förlustfunktion att minimera för vårt ordboksbaserade problem, och vi undersöker empiriskt LAMP:s kapacitet under olika förhållanden: vi varierar datasetets storlek, antalet underbärare, nätverkets djup och signal-brusförhållandet (SNR). Vi föreslår också ett nytt nätverk kallat Adaptive-LAMP som skiljer sig från LAMP genom att det införs ett litet neuralt nätverk i varje lager för att uppskatta vissa parametrar i stället för att lära sig dem. Experiment visar att LAMP presterar betydligt bättre än AMP när det gäller NMSE vid låga signal-brusförhållande (SNR) och sämre vid höga SNR-nivåer. Intressant nog presterar båda de föreslagna nätverken bra när det gäller att upptäcka aktiva vägar i cellulära nätverk, vilket banar väg för nya metoder för kanalskattningsproblemet.

La coopération des émetteurs est considérée comme une approche prometteuse pour limiter les interférences dans les réseaux sans fil ayant une réutilisation des ressources spectrales très agressive. La coopération des émetteurs permet l'optimisation conjointe de certains paramètres de transmission. Bien que la coopération des émetteurs existe sous différentes formes, une hypothèse commune est le besoin pour les émetteurs entrant en coopération (i) d’acquérir et de partager des informations concernant le canal de propagation ainsi que (ii) d’effectuer une coopération fondée sur les informations diffusées à l'étape précédente. La conception coordonnée des matrices de précodage et, d’une manière encore plus marquée, la transmission conjointe à différents émetteurs sont des exemples importants de méthodes de coopérations présentant ces propriétés. L’acquisition et l'échange de l’information de canal étant strictement limités, il se pose deux questions importantes: (i) Quelle information doit être renvoyée ou échangée de manière à permettre la coopération la plus efficace? (ii) Quelles méthodes permettent de réaliser les gains de la coopération dans ce contexte de partage limité et imparfait d’information? Dans cette thèse, nous abordons les deux questions précédentes. Dans un premier temps, nous considérons que chaque émetteur acquiert une estimée de canal imparfaite. Dans un second temps, nous étudions la conception de techniques de coopération efficaces dans une configuration d’information de canal imparfaitement partagée entre les émetteurs. Enfin, les futures directions de recherche découlant de ces travaux sont présentées et discutées. / Transmitter cooperation is considered a promising tool for dealing with interference in wireless networks with an aggressive reuse policy of spectral resources. Although transmitter cooperation comes in many flavors, a recurrent assumption behind proposed methods lies in the need for cooperating devices to (i) acquire, share information pertaining to the propagation channel toward the multiple receivers and (ii) perform cooperation based on the disseminated information in the previous step. This holds true for instance for coordinated beamforming methods and, to an even greater extent, for network-MIMO (Joint Processing coordinated multi-point (JP CoMP) in the long term evolution (LTE) terminology). As feedback and exchange of channel state information (CSI) come at a price in terms of signaling overhead, there arise two important questions: (i) What information should be fed back or exchanged such that the CSI acquired at each transmitter is most informative to perform cooperation? (ii) Which techniques can reap the benefits of cooperation while living with an imperfect channel representation that varies from transmitter to transmitter ? In this thesis, we address both aforementioned questions. We consider first each transmitter acquires an initial imperfect CSI based on limited receivers feedback. For the design of efficient cooperation techniques that copes with the imperfect and non-identical CSI configuration at each transmitter, we investigate specifically a regularized zero forcing (RZF) precoder design in large system scenario. Finally, interesting and challenging research directions and open problems are discussed.

With the recent advancements of wireless networks to satisfy new requirements, the investigation of novel transmission schemes to improve the link level performance is of major importance. A very common technique utilized in nowadays systems is the Orthogonal frequency division multiplexing (OFDM) waveform, which has been adopted by several standards, including WiFi, LTE, and more recently 5G, due to its simple equalization process. Despite its success, this dissertation shows that OFDM is a sub-optimal scheme under frequency-selective channel (FSC), when channel state information (CSI) is available at the receiver only. Based on the coded modulation capacity approach, this work demonstrates that the data symbols should experience the same channel gain in order to achieve the best performance, leading to the equal gain criterion (EGC). However, this comes at a cost in terms of losing orthogonality among data symbols. The result is valid for linear modulation matrices under the assumptions of CSI at only at the receiver with perfect feedback equalization. In order to attain the EGC for doubly-dispersive channels, the block multiplexing (BM) waveform is proposed in this thesis, where the data symbols are spread in frequency and time. For instance, the recently conceived orthogonal time frequency space (OTFS) is shown to be a particular case of BM with the classical single-carrier (SC). Regarding the equalization for the robust waveforms, it is shown that the minimum mean squared error with parallel interference cancellation (MMSE-PIC) employed together with convolutional encoder and soft decoder can completely remove the inter-symbol interference (ISI), where a low-complexity implementation is designed. In addition, a waveform with decreased complexity based on the sparse Walsh-Hadamard (SWH) is proposed for two reasons, i) sparse spreading requires a transform with lower size, ii) the Walsh-Hadamard transform is implemented with 1s and −1s, which requires less complexity than fast Fourier transform (FFT) based waveforms. Furthermore, the problem of estimating the time varying channel is considered, where a unique word (UW) or (pilot block) based approach is studied. In this regard, another main contribution of this dissertation is to develop an optimization framework, where the combination of channel estimation plus Doppler spread error is minimized. In particular, the composite error minimization is achieved by properly setting the FFT size of the system, for a fixed data length. Lastly, cyclic prefix (CP)-free system is considered such that the transmission time is decreased, and therefore provides a better channel estimation. Naturally, the CP-free system has undesirable interference, which is resolved by an iterative CP-Restoration algorithm. In this case, we extend the EGC to equal reliability criterion (ERC), i.e., the data symbols should be equally reliable and not only have equal gain. As a consequence, the BM with orthogonal chirp division multiplexing (OCDM) waveform has the best performance due to equal time and frequency spreading. In conclusion, the coded modulation capacity approach of this dissertation provides new insights and solutions to improve the performance of wireless systems.

The increasing demand for mobile data has placed significant strain on radio access networks (RANs), leading to a continuous need for increased network capacity. In keeping with that, a significant advancement in modern RANs is the ability to utilize several receivers and transmitters, to allow for beamforming. One way to increase the capacity of the network is therefore to optimize the resource allocation by preprocessing the transmitted signals, which involves several costly matrix multiplications (MMs). The aim of the project was to investigate the potential of accelerating Ericsson's RAN simulations by using AI based approximate matrix multiplication (AMM) algorithms. The main focus was on the multiply additionless (MADDNESS) algorithm, a product quantization technique that has achieved speedups of up to 100 times compared to exact MM, and 10 times faster than previous AMM methods. A complex matrix handling version of MADDNESS was implemented in Java and Python respectively, and its speed and accuracy were evaluated against Ericsson's current MM implementation. The proposed implementation did not beat the benchmark with respect to speed, instead resulting in a 4-10 times slowdown in runtime. However, this may largely be due to the fact that the used languages do not allow for complete control over memory resource allocation. As such, the implementations at hand do not incorporate all the crucial features of the algorithm. Particularly, the handicapped version does not fully leverage the vectorization potential, which is one of the key contributors to the speed of the algorithm. Consequently, further improvements are necessary before employing the techniques in an end-to-end implementation. / Den växande efterfrågan på mobildata har ökat belastningen på dagens radionätverk (RAN) och har medfört ett behov av att utvidga dess kapacitet. En betydande innovation inom RAN är beamforming, vilket är förmågan att fokusera digitala signaler mot mottagaren och på så vis öka singalstyrkan. En metod för att öka kapaciteten i ett nätverk är att optimera både kvaliteten av och resursallokeringen mellan nätverkets digitala kanaler, vilket medför tidskrävande matrismultiplikationer. Syftet med denna studie var att utforska om AI-baserade approximativa matrismultiplikationsalgoritmer har potentialen att accelerera Ericssons digitala tvilling-simuleringar. Studien fokuserade i huvudsak på produktkvantiseringsalgoritmen MADDNESS som påvisat potentialen att accelerera exakta matrismultiplikationer med en faktor 100, samt en faktor 10 snabbare än jämförbara approximativa metoder. En modifierad version av MADDNESS, som behandlar komplexa matriser, implementerades i Java samt Python, varefter precisionen och hastigheten utvärderades. Den föreslagna implementationen resulterade i en försämring med avseende på hastigheten med en faktor 4-10 jämfört med Ericssons nuvarande algoritmer. Den föreslagna implementationen saknar effektiv minnesallokering och misslyckas följaktligen att till fullo ta tillvara på vektoriseringspotentialen i MADDNESS. Detta indikerar att det är nödvändigt för ytterligare förbättringar innan algoritmen är användbar i den givna simuleringsmiljön.

The onset of quantum computing threatens commonly used schemes for information secrecy across wireless communication channels, particularly key-based data-level encryption. This calls for secrecy schemes that can provide everlasting secrecy resistant to increased computational power of an adversary. One novel physical layer scheme proposes that an intended receiver capable of performing analog cancellation of a known key-based interference would hold a significant advantage in recovering small underlying messages versus an eavesdropper performing cancellation after analog-to-digital conversion. This advantage holds even in the event that an eavesdropper can recover and use the original key in their digital cancellation. Inspired by this scheme, a flexible software-defined radio receiver design capable of maintaining analog cancellation ratios consistently over 40 dB, reaching up to and over 50 dB, is implemented in this thesis. Maintaining this analog cancellation requires very precise time-frequency synchronization along with accurate modeling and simulation of the channel effects on the interference. The key sources of synchronization error preventing this test bed from achieving and maintaining perfect interference cancellation, sub-sample period timing errors and limited radio frequency stability, are explored for possible improvements. To further prove robustness of the implemented secrecy scheme, the testbed is shown to operate with both phase-shift keying and frequency-modulated waveforms. Differences in the synchronization algorithm used for the two waveforms are highlighted. Interference cancellation performance is measured for increasing interference bandwidth and shown to decrease with such. The implications this testbed has on security approaches based on intentional interference employed to confuse eavesdroppers is approached from the framework proposed in the motivating everlasting secrecy scheme. Using analog cancellation levels from the hardware testbed, it is calculated that secrecy rates up to 2.3 bits/symbol are gained by receivers (intended or not) performing interference cancellation in analog rather than on a digital signal processor. Inspired by the positive gains in secrecy over systems not performing analog cancellation prior to signal reception, a novel secrecy scheme that focuses on the advantage an analog canceller holds in receiver amplifier compression is proposed here. The adversary amplifier is assumed to perform linear cancellation after the interference has passed through their nonlinear amplifier. This is accomplished by deriving the distribution of the interference residual after undergoing an inverse tangent transfer function and perfect linear cancellation. Parameters of this scheme are fit for the radios and cancellation ratios observed in the testbed, resulting in a secrecy gain of 0.95 bits/symbol. The model shows that larger message powers can still be kept secure for the achieved levels of cancellation, thus providing an even greater secrecy gain with increased message transmission power.

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<p>Future wireless applications will require networks to provide high rates, reduced power consumption, reliable communications, and low latencies in a wide range of deployment scenarios. To support the never-ending growth in wireless data traffic, a solution is to operate wireless networks on the wide bandwidth available at higher frequencies, e.g., millimeter wave (mmWave) and sub-terahertz (sub-THz) bands. However, new challenges arise as networks operating at higher frequencies experience harsher propagation characteristics. To compensate for such severe signal attenuation, the directional beamforming via massive multipleinput multiple-output (MIMO) is adopted to provide array gains, but it necessitates accurate MIMO channel state information incurring unacceptably large training overhead. Wireless system engineers will require to develop fast and efficient channel training algorithms for massive MIMO systems. Another new challenge arises in scenarios without a direct link between the source and destination due to serious pathloss, which requires cooperative relay beamforming to enhance the communication coverage. The beamforming weights of the distributed relays and the receive combiner can be jointly optimized to enhance Quality-of-Service in multi-user relay beamforming networks. Our contributions cover three specific topics as follows: First, we develop a learning-based beam alignment approach, which enables the position-aided beam recommendation to support users at new positions, to reduce the training overhead in MIMO systems. Second, we propose a compressed training framework to estimate the time-varying sub-THz MIMO-OFDM channels with dual-wideband effect. Lastly, we propose a joint relay beamforming and receive combiner design, considering an optimization problem formulation that maximizes the minimum of the receiving signal-to-interference-plus-noise ratios among multiple users. In each specific topic, we provide the algorithms and show the numerical results to demonstrate the improved performance over the state-of-the-art techniques.</p>

Gaussian processes (GPs) as a Bayesian regression method have been around for some time. Since proven advant-ageous for sparse and noisy data, we explore the potential of Gaussian process regression (GPR) as a tool for estimating radiochannel characteristics. Specifically, we consider the estimation of a time-varying continuous transfer function from discrete samples. We introduce the basic theory of GPR, and employ both GPR and its deep-learning counterpart deep Gaussian process regression (DGPR)for estimation. We find that both perform well, even with few samples. Additionally, we relate the channel coherence bandwidth to a GPR hyperparameter called length-scale. The results show a tendency towards proportionality, suggesting that our approach offers an alternative way to approximate the coherence band-width. / Gaussiska processer (Gaussian processes, GPs) har länge använts för Bayesiansk regression. Då de visat sig fördelaktiga för gles och brusig data utforskar vi möjligheterna för GP-regression (Gaussian process regression, GPR) som ett verktyg för att estimera egenskaper hos radiokanaler.I synnerhet betraktas skattning av en tidsvarierande överföringsfunktion utifrån diskreta samplingar. Vi presenterar den grundläggande teorin kring GPR, och använder både GPR och dess djupinlärningsmotsvarighet DGPR (deep Gaussian process regression) för skattning. Båda ger goda resultat, även när samplingarna är få. Utöver detta så relaterar vi koherensbandbredden hos en radiokanal till en hyperparameter i GPR-modellen. Resultaten visar på en tendens till proportionalitet, vilket antyder att vår metod kan användas som ett alternativt sätt att approximera koherensbandbredden. / Kandidatexjobb i elektroteknik 2020, KTH, Stockholm

In the sixth generation of wireless communication systems (6G), there exist multiple candidate enabling technologies that help the wireless network satisfy the ever-increasing demand for speed, coverage, reliability, and mobility. Among these technologies, reconfigurable intelligent surfaces (RISs) extend the coverage of a wireless network into dead zones, increase capacity, and facilitate integrated sensing and communications tasks by consuming very low power, thus contributing to energy efficiency as well. RISs are meta-material-based devices whose electromagnetic reflection characteristics can be controlled externally to cater to the needs of the communication links. Most ubiquitously, this comes in the form of adding a desired phase shift to an incident wave before reflecting it, which can be used to phase-align multiple incident waves to increase the strength of the signal at the receiver and provide coverage to an area that otherwise would be a dead zone. While this portrays an image of a dream technology that would boost the existing wireless networks significantly, RISs do not come without engineering problems. First of all, the individual elements do not exhibit ideal reflection characteristics, that is, they attenuate the incident signal in a fashion depending on the configured phase shift. This creates the phenomenon called "phase-dependent amplitude". Another problem caused by RISs is the channel estimation overhead. In a multiple-antenna communication system, the channel between two terminals is as complex as the product of the number of antennas at each end. However, when an RIS comes into the equation, the cascade of the transmitter-RIS and RIS-receiver channels has a complexity further multiplied by the number of RIS elements. Consequently, the channel estimation process to utilize the RIS effectively becomes more demanding, that is, more pilot signals are required to estimate the channel for coherent reception. This adversely affects the effective data rate within a communication system since more resources need to be spent for pilot transmission and fewer resources can be allocated for data transmission. While there exists some work on reducing the channel dimensions by exploiting the channel structure, this problem persists for unstructured channels. In addition, for the wireless networks using multiple RISs, a new kind of pilot contamination arises, which is the main topic of this thesis. In the first part of this thesis, we study this new kind of pilot contamination in a multi-operator context, where two operators provide services to their respective served users and share a single site. Each operator has a single dedicated RIS and they use disjoint frequency bands, but each RIS inadvertently reflects the transmitted uplink signals of the user equipment devices in multiple bands. Consequently, the concurrent reflection of pilot signals during the channel estimation phase introduces a new inter-operator pilot contamination effect. We investigate the implications of this effect in systems with either deterministic or correlated Rayleigh fading channels, specifically focusing on its impact on channel estimation quality, signal equalization, and channel capacity. The numerical results demonstrate the substantial degradation in system performance caused by this phenomenon and highlight the pressing need to address inter-operator pilot contamination in multi-operator RIS deployments. To combat the negative effect of this new type of pilot contamination, we propose to use orthogonal RIS configurations during uplink pilot transmission, which can mitigate or eliminate the negative effect of inter-operator pilot contamination at the expense of some inter-operator information exchange and orchestration. In the second part of this thesis, we consider a single-operator-two-RIS integrated sensing and communication (ISAC) system where the single user is both a communication terminal and a positioning target. Based on the uplink positioning pilots, the base station aims to estimate both the communication channel and the user's position within the indoor environment by estimating the angle of arrival (AoA) of the impinging signals on both RISs and then exploiting the system and array geometries to estimate the user position and user channels respectively. Although there is a single operator, due to the presence of multiple RISs, pilot contamination occurs through the same physical means as multi-operator pilot contamination unless the channel estimation process is parameterized. Since the communication links are considered to be pure line-of-sight (LOS), their structure allows the reduction of the number of unknown parameters. Consequently, the reduction of information caused by pilot contamination does not affect the channel estimation procedure, hence the pilot contamination is overcome. On the other hand, the position of the user is determined by intersecting the lines drawn along the AoA estimates. We adopt the Cramér-Rao Lower Bound (CRLB), the lower bound on the mean squared error (MSE) of any unbiased estimator, for both channel estimation and positioning. Our numerical results show that it is possible to utilize positioning pilots for parametric channel estimation when the wireless links are LOS. / <p>QC 20240416</p>