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This thesis presents new user association (UA) schemes that take cell interference into account for a multi-cell network aided with multiple reconfigurable intelligent surfaces (RISs). We formulate a network spectral efficiency maximization problem by jointly optimizing active beamforming at the base stations (BSs), passive beamforming at the RISs, and user-BS association with consideration to the impact of RISs. We then propose a computationally efficient iterative algorithm based on alternating optimization to resolve this intractable mixed-integer non-convex problem. A fractional programming technique is used to optimize active beamforming at the BSs and passive beamforming at the RISs, and a penalization method combined with successive convex programming is applied for UA optimization, which is shown to achieve an optimal solution. Additionally, we balance BS loads and maximize the network utility by optimizing the user association with a matching game in another scheme. Finally, a crucial aspect of 6G is that localization and sensing will not be a by-product of communications development but will instead be integrated into the system from the start, and thus is a main design target of 6G. Toward this, a vision for how location and sensing information can be used to support, enable, and enrich novel applications will be sketched. In addition, the potential benefits of location and sensing information for improving communications are investigated as use cases. Therefore, taking advantage of sensing with radio waves and localization, we propose a novel environment-aware joint active/passive beamforming approach for RIS-aided wireless communication based on the new concept of channel knowledge map (CKM). In the proposed scheme, the user equipment location information is combined with the radio environment information provided by CKM to achieve efficient beamforming without real-time training. Simulation results show the proposed scheme’s superior performan, Tiivistelmä Tässä väitöskirjassa esitellään uusia käyttäjäyhteys (user association, UA)- järjestelmiä, joissa otetaan huomioon solujen häiriöt monisoluverkossa, jossa on useita uudelleenkonfiguroitavia älykkäitä pintoja (reconfigurable intelligent surfaces, RIS). Muotoilemme verkon spektritehokkuuden maksimoinnin ongelman optimoimalla yhdessä aktiivisen keilanmuodostuksen tukiasemilla, passiivisen keilanmuodostuksen RIS-pinnoilla ja käyttäjän tukiasemayhteyden ottaen huomioon RIS-pintojen vaikutuksen. Tämän jälkeen ehdotamme laskennallisesti tehokasta iteratiivista algoritmia, joka perustuu vuorottelevaan optimointiin, jotta tämä hankala ei-konveksi sekakokonaislukuongelma saadaan ratkaistua. Tukiasemien aktiivisen keilanmuodostuksen ja RIS-pintojen passiivisen keilanmuodostuksen optimointiin käytetään fraktionaalista ohjelmointitekniikkaa, ja peräkkäiseen konveksiin ohjelmointiin yhdistettyä rangaistusmenetelmää sovelletaan käyttäjäyhteyden optimointiin. Sen avulla osoitetaan päästävän optimaaliseen ratkaisuun. Lisäksi tasapainotamme tukiasemien kuormia ja maksimoimme verkon käytön optimoimalla käyttäjäyhteyden vastaavalla pelillä toisessa järjestelmässä. 6G:n keskeinen näkökohta on, että paikantaminen ja tunnistaminen eivät ole viestinnän kehityksen sivutuotteita, vaan ne integroidaan järjestelmään alusta alkaen, ja ne ovat siten 6G:n suunnittelun päätavoitteita. Tätä varten hahmotellaan visio siitä, miten sijainti- ja tunnistamistietoja voidaan käyttää uusien sovellusten tukemiseen, mahdollistamiseen ja rikastamiseen. Lisäksi käyttötapauksina tutkitaan paikannus- ja tunnistamistietojen mahdollisia hyötyjä viestinnän parantamisessa. Tämän vuoksi ja radioaaltojen avulla tapahtuvaa tunnistamista ja paikannusta hyödyntämällä ehdotamme uutta ympäristötietoista yhdistettyä aktiivista/passiivista keilanmuodostusta RIS-avusteiseen langattomaan viestintään uuden kanavatietokartan konseptin perusteella. Ehdotetussa järjestelmässä käyttäjien laitteiden sijaintitiedot yhdist

Proactive and intelligent management of network resource utilization (RU) using deep learning (DL) can significantly improve the efficiency and performance of the next generation of wireless networks. However, variations in wireless RU are often affected by uncertain events and change points due to the deviations of real data distribution from that of the original training data. Such deviations, which are known as dataset drifts, can subsequently lead to a shift in the corresponding decision boundary degrading the DL model prediction performance. To address these challenges, we present hardware-accelerated real-time radio frequency (RF) analytics and drift-awareness modules for robust DL predictions. We have prototyped the proposed design on a Zynq-7000 System-on-Chip that contains an FPGA and an embedded ARM processor. We have used Xilinx Vivado design suite for synthesis and analysis of the HDL design for the proposed solution. To detect dataset drifts, the proposed solution adopts a distance-based technique on FPGA to quantify in real-time the change between the prediction distribution obtained from DL predictions and data distribution of input streaming samples. Using various performance metrics, we have extensively evaluated the performance of the proposed solution and shown that it can significantly improve the DL model robustness in the presence of dataset drifts.

The aim of this thesis is to devise novel promising frameworks, e.g., statistical analyses, as well as performing optimization algorithms, and applying novel machine learning (ML) methods to realize stringent ultra-reliable and low-latency communication (URLLC) requirements, e.g., for use in factories and mission-critical applications with smart wireless networks assisted by re-configurable intelligent surface (RIS) technologies. First, we study the average achievable finite blocklength (FBL) rate and error probability of an RIS-aided system. The distribution of the received signal-to-noise ratio (SNR) is matched to a Gamma random variable whose parameters depend on the total number of RIS elements. The performance loss due to the presence of phase errors arising from limited quantization levels in the RIS elements is discussed. The required number of RIS elements to achieve a desired error probability in the FBL regime is derived and the advantages of RIS technology for URLLC systems are highlighted. In the second part of the thesis, we study a multi-objective optimization problem for maximizing the achievable FBL rate while minimizing the transmission time in an RIS-assisted short packet system. The two objective functions are the total FBL rate with a target error probability and minimizing the total used channel blocklengths (CBLs). A fundamental trade-off between maximizing the achievable rate in the FBL regime and reducing the transmission duration was shown. Additionally, the applicability of RIS in reducing the used CBLs while increasing the achievable rate is emphasized. Finally, a joint active/passive beamforming and CBL optimization in a non-ideal RIS-aided URLLC system is analyzed using novel ML techniques. Specifically, we leverage an actor-critic policy gradient deep reinforcement learning algorithm named twin-delayed deep deterministic policy gradient (TD3). We show that optimizing the RIS phase shifts, base station beamforming, and CBL varia, Tiivistelmä Tässä väitöskirjassa tutkitaan uudelleenkonfiguroituvien älykkäiden pintojen (RIS) soveltamista erittäin luotettavien pienen viiveen tiedonsiirron (URLLC) ongelmiin. Väitöskirjassa kehitetään tilastollisen analyysin menetelmiä, optimointialgoritmeja ja koneoppimismenetelmiä (ML), joita voidaan soveltaa esimerkiksi teollisuudessa ja tehtäväkriittisissä RIS-teknologiaa hyödyntävissä älykkäiden langattomien verkkojen sovelluksissa. Väitöskirjan ensimmäisessä osassa tutkitaan RIS-avusteisen järjestelmän keskimääräistä rajallisella lohkopituudella (FBL) saavutettavissa olevaa nopeutta sekä virhetodennäköisyyttä. Väitöstyössä vastaanotetun signaalin signaali-kohinasuhteen (SNR) jakauma sovitetaan Gamma-satunnaismuuttujaan jakaumaan, jonka parametrit riippuvat RIS-elementtien kokonaismäärästä. Väitöskirjassa käsitellään myös RIS-elementtien vaiheen kvantisoinnista johtuvaa virhettä ja suorituskyvyn menetystä. Lisäksi väitöskirjassa johdetaan FBL-järjestelmän tietyn tavoitevirhetodennäköisyyden saavuttamiseksi vaadittava RIS-elementtien lukumäärä, ja painotetaan RIS-tekniikan käytön hyötyjä URLLC-järjestelmissä. Väitöskirjan toisessa osassa tutkitaan monitavoitteista optimointiongelmaa, jossa samanaikaisesti pyritään maksimoimaan saavutettavissa oleva FBL-nopeus sekä minimoida RIS-avusteisen lyhytpakettijärjestelmän lähetysaika. Optimointiongelmassa tavoitteina ovat siten FBL-kokonaisnopeuden maksimointi tietylle virhetodennäköisyydelle ja käytetyn kanavan lohkopituuden (CBL) minimointi. Väitöskirjassa osoitettiin, että näiden kahden tavoitteen välillä joudutaan aina tekemään perustavanlaatuinen kompromissi. Väitöstyössä korostetaan RIS-pintojen soveltuvuutta samanaikaiseen CBL:n lyhentämiseen ja saavutettavissa olevan FBL-nopeuden lisäämiseen. Väitöskirjan kolmannessa ja viimeisessä osassa analysoidaan aktiivisen/passiivisen keilanmuodostuksen ja CBL:n samanaikaista optimointia epäideaalisessa RIS-avusteisessa URLLC-järjestelmässä käyttäen uusia koneoppimismene

Terahertz (THz) spectrum is identified as a potential enabler for advanced sensing and positioning, where THz-Time domain spectroscopy (THz-TDS) is specified for investigating the unique material properties. The transmission THz-TDS measures the light absorption of materials. This paper proposes a novel low-complex deep neural network (DNN)-based multi-class classification architecture to sense a wide variety of materials from the transmission spectroscopy. Based on the spectroscopic measurements made across a chosen THz region of interest, DNN extracts and learns the distinctive crystal structure of materials as features. With sufficient quantities of noisy spectroscopic data and labels, we train and validate the model. In low SNR regions, the proposed DNN classification architecture achieves about 92% success rate, which is greater than those of the state-of-the-art methods.

Effective resource allocation is a crucial requirement to achieve the stringent performance targets of ultra-reliable low-latency communication (URLLC) services. Predicting future interference and utilizing it to design efficient interference management algorithms is one way to allocate resources for URLLC services effectively. This paper proposes an empirical mode decomposition (EMD) based hybrid prediction method to predict the interference and allocate resources for downlink based on the prediction results. EMD is used to decompose the past interference values faced by the user equipment. Long short-term memory and auto-regressive integrated moving average methods are used to predict the decomposed components. The final predicted interference value is reconstructed using individual predicted values of decomposed components. It is found that such a decomposition-based prediction method reduces the root mean squared error of the prediction by 20–25%. The proposed resource allocation algorithm utilizing the EMD-based interference prediction was found to meet near-optimal allocation of resources and correspondingly results in 2–3 orders of magnitude lower outage compared to state-of-the-art baseline prediction algorithm-based resource allocation.

Reconfigurable intelligent surface (RIS)-aided communication is a promising technology for 6G systems to reconfigure the propagation environment proactively. However, it requires efficient real-time channel training, which suffers excessive overhead. To resolve this challenge, taking advantage of sensing with radio waves and localization, we propose a novel environment-aware joint active/passive beamforming for RIS-aided wireless communication based on the new concept of channel knowledge map (CKM). In the proposed scheme, the user equipments (UEs) location information is combined with the radio environment information provided by CKM to achieve efficient beamforming without real-time training. Simulation results show the proposed scheme’s superior performance over training-based beamforming, which is also quite robust to errors related to UE’s location in practice.

We propose a novel scheme that allows MIMO system to modulate a set of permutation matrices to send more information bits, extending our initial work on the topic. This system is called Permutation Matrix Modulation (PMM). The basic idea is to employ a permutation matrix as a precoder and treat it as a modulated symbol. We continue the evolution of index modulation in MIMO by adopting all-antenna activation and obtaining a set of unique symbols from altering the positions of the antenna transmit power. We provide the analysis of the achievable rate of PMM under Gaussian Mixture Model (GMM) distribution and finite cardinality input (FCI). Numerical results are evaluated by comparing PMM with the other existing systems. We also present a way to attain the optimal achievable rate of PMM by solving a maximization problem via interior-point method. A low complexity detection scheme based on zero-forcing (ZF) is proposed, and maximum likelihood (ML) detection is discussed. We demonstrate the trade-off between simulation of the symbol error rate (SER) and the computational complexity where ZF performs worse in the SER simulation but requires much less computational complexity than ML.

A deep learning-based resource allocation algorithm that maximizes the sum rate of a limited fronthaul cell-free massive MIMO network with transceiver hardware impairments is proposed in this letter. The sum rate maximization problem with user power constraints and total fronthaul capacity constraints for channel state information (CSI) and data transmission is considered. The deep neural network (DNN) PowerNet is proposed to learn solutions to the joint power control and capacity allocation problem in a low-complex, flexible, and scalable way. An unsupervised learning approach is used which eliminates the need of knowing the optimal resource allocation vectors during model training, hence having a simpler and more flexible model training stage. Numerical simulations show that PowerNet achieves close sum rate performance compared to the existing optimization-based approach, with a significantly lower time complexity which does not exponentially scale with the number of users and access points (APs) in the network. Furthermore, the addition of the online learning stage resulted in a better sum rate than the optimization-based method.

Polar codes have received growing attention in the past decade and have been selected as the coding scheme for the control channel in the fifth generation (5G) wireless communication systems. However, the conventional polar codes have only been constructed by binary ( 2×2 ) kernel, which poses block length limitation to powers of 2. To attain more flexible block lengths, multi-kernel polar codes are proposed. In this paper, a combinational architecture for multi-kernel polar codes with high throughput is proposed based on successive cancellation decoding algorithm. The proposed scheme can decode pure-binary, pure-ternary ( 3×3 ), and binary-ternary mixed polar codes. The decoder’s architecture is rate-flexible, meaning that a new code rate can be assigned to the decoder at every clock cycle. The proposed architecture is validated by FPGA implementation, and the results reveal that a code of size N=81 achieves the coded throughput of 1664.5 Mbps. A Python-based polar compiler is also proposed to automatically generate the HDL modules for target decoders. A designer can input the target block length and kernel ordering of a polar code and get the required VHDL files automatically.

The linear combination of Student’s t random variables (RVs) appears in many statistical applications. Unfortunately, the Student’s t distribution is not closed under convolution, thus, deriving an exact and general distribution for the linear combination of \(K\) Student’s t RVs is infeasible, which motivates a fitting/approximation approach. Here, we focus on the scenario where the only constraint is that the number of degrees of freedom of each t − RV is greater than two. Notice that since the odd moments/cumulants of the Student’s t distribution are zero and the even moments/cumulants do not exist when their order is greater than the number of degrees of freedom, it becomes impossible to use conventional approaches based on moments/cumulants of order one or higher than two. To circumvent this issue, herein we propose fitting such a distribution to that of a scaled Student’s t RV by exploiting the second moment together with either the first absolute moment or the characteristic function (CF). For the fitting based on the absolute moment, we depart from the case of the linear combination of \(K = 2\) Student’s t RVs and then generalize \(K ≥ 2\) to through a simple iterative procedure. Meanwhile, the CF-based fitting is direct, but its accuracy (measured in terms of the Bhattacharyya distance metric) depends on the CF parameter configuration, for which we propose a simple but accurate approach. We numerically show that the CF-based fitting usually outperforms the absolute moment-based fitting and that both the scale and number of degrees of freedom of the fitting distribution increase almost linearly with \(K\).

Massive machine-type communications (mMTC) in 6G requires supporting a massive number of devices with limited resources, posing challenges in efficient random access. Grant-free random access and uplink non-orthogonal multiple access (NOMA) are introduced to increase the overload factor and reduce transmission latency with signaling overhead in mMTC. Sparse code multiple access (SCMA) and Multi-user shared access (MUSA) are introduced as advanced code domain NOMA schemes. In grant-free NOMA, machine-type devices (MTD) transmit information to the base station (BS) without a grant, creating a challenging task for the BS to identify the active MTD among all potential active devices. In this paper, a novel pre-activated residual neural network-based multi-user detection (MUD) scheme for the grant-free SCMA and MUSA system in an mMTC uplink framework is proposed to jointly identify the number of active MTDs and their respective messages in the received signal’s sparsity and the active MTDs in the absence of channel state information. A novel residual unit designed to learn the properties of multi-dimensional SCMA codebooks, MUSA spreading sequences, and corresponding combinations of active devices with diverse settings. The proposed scheme learns from the labeled dataset of the received signal and identifies the active MTDs from the received signal without any prior knowledge of the device sparsity level. A calibration curve is evaluated to verify the model’s calibration. The application of the proposed MUD scheme is investigated in an indoor factory setting using four different mmWave channel models. Numerical results show that when the number of active MTDs in the system is large, the proposed MUD has a significantly higher probability of detection compared to existing approaches over the signal-to-noise ratio range of interest.

Intelligent reflecting surfaces (IRSs) are a promising technology for future-generation wireless networks by extending coverage region to blind spots and increasing mmWave propagation paths in non-line of sight environments. User association (UA) in dense millimeter wave (mmWave) networks is vital to characterizing connections among base stations (BSs) and mobile users for load balancing, interference management, and maximizing network utility. However, it has yet to be examined thoroughly in a multi-IRS-aided network. This paper presents a new UA scheme that takes cell interference into account for a multi-cell mmWave cellular network aided with multiple IRSs. We formulate a network spectral efficiency maximization problem by jointly optimizing active beamforming (AB) at the BSs, passive beamforming (PB) at the IRSs, and user-BS association with consideration of the impact of IRSs. We then propose a computationally efficient iterative algorithm based on alternating optimization (AO) to resolve this intractable mixed-integer non-convex problem. A fractional programming technique is used to optimize active beamforming at the BSs and passive beamforming at the IRSs, and a penalization method combined with successive convex programming is applied for UA optimization, which is shown to reach the optimal solution. Simulation results show significant performance improvements obtained by the proposed algorithm, providing higher spectral efficiency compared to several benchmark algorithms, while having a low computational complexity.

A graph neural network (GNN) based access point (AP) selection algorithm for cell-free mas-sive multiple-input multiple-output (MIMO) systems is proposed. Two graphs, a homogeneous graph which includes only AP nodes representing the structure of the APs in the network, and a heterogeneous graph which includes both AP nodes and user equipment (UE) nodes are constructed to represent a cell-free massive MIMO network. A GNN based on the inductive graph learning framework GraphSAGE is used to obtain the embed-dings which are then used to predict the links between the nodes. The numerical results show that compared to the proximity-based AP selection algorithms, the proposed GNN based algorithm predicts the potential APs with more accuracy. Compared to the large scale fading coefficient based AP selection algorithms, the proposed algorithm does not require measured and sorted signal strengths of all the neighbouring APs. Furthermore, the proposed algorithm is scalable in terms of the number of users in the cell-free system.

Unmanned aerial vehicles (UAVs) are becoming increasingly attractive for the ambitious expectations for 5G and beyond networks due to their several benefits. Indeed, UAV-assisted communications introduce a new range of challenges and opportunities regarding the security of these networks. Thus, in this paper we explore the opportunities that UAVs can provide for physical layer security solutions. Particularly, we analyse the secrecy performance of a ground wireless communication network assisted by N friendly UAV jammers in the presence of an eavesdropper. To tackle the secrecy performance of this system, we introduce a new area-based metric, the weighted secrecy coverage (WSC), that measures the improvement on the secrecy performance of a system over a certain physical area given by the introduction of friendly jamming. Herein, the optimal 3D positioning of the UAVs and the power allocation is addressed in order to maximise the WSC. For that purpose, we provide a reinforcement learning-based solution by modelling the positioning problem as a multi-armed bandit problem over three positioning variables for the UAVs: angle, height and orbit radius. Our results show that the proposed algorithm improves the secrecy of the system over time in terms of the WSC, and it converges into a stable state close to the exhaustive search solution for discretised actions, where there is a trade-off between expediency of the positioning of the UAVs to positions of better secrecy outcome and energy consumption.

While the fifth generation (5G) New Radio wireless system is being deployed across the globe, the wireless research community has started exploring what will the sixth generation (6G) be? 6G is expected to cater to societal and economic needs by integrating the biological, physical, and virtual worlds with the networks. The notion of multi-service communication introduced in 5G will be further diversified and expanded to include new service classes representing novel and emerging use cases that were not considered as a part of the 5G landscape. Ultra-reliable low-latency communications (URLLC) and their evolution in 6G are of particular interest given their prominent role in enabling future industrial Internet of Things (IIoT) applications. During 2020‐2022, ETRI from South Korea and the University of Oulu from Finland convened a joint project to explore URLLC evolution towards the 6G era. This article summarizes the key highlights of the project and outlines its main findings. The project involved a study of future URLLC service classes and their potential enablers, along with a demonstration of remote monitoring of a smart factory over an intercontinental link. The project’s proof-of-concept was able to demonstrate remote monitoring of ETRI’s IIoT testbed from Finland at sub-300 ms service round trip time.

Polar codes have been receiving increased attention for application in beyond 5G networks. They offer low-complexity decoding algorithm and can achieve symmetric channel capacity. However, the majority of research works have focused on the codes constructed by the binary kernel (2×2 polarization matrix) which bounds the code length to an integer power of 2. Multi-kernel polar codes have been proposed as a method that allows the construction of polar codes with sizes different from powers of 2 by mixing multiple kernels of different dimensions. A hardware implementation based on the successive cancellation (SC) algorithm found in the literature shows that it suffers from a long decoding latency. In this paper, we design and implement a multi-kernel decoder based on the fast-simplified SC (fast-SSC) algorithm to decrease the decoding latency. It can decode any code constructed by binary and ternary (3×3) kernels featuring flexible code length, code rate, and kernel sequence. FPGA implementation results reveal that a polar code of length N = 1536, rate R = 1/2 with Processing Element (Pₑ) value of Pₑ = 240, gains 84.6% lower latency compared to the original algorithm. Also, the architecture supports polar codes constructed by purely-binary and purely-ternary kernels. A polar code of length N = 1024, rate R = 1/2, and Pₑ =120 achieves an information throughput of 432 Mbps

We study the joint active/passive beamforming and channel blocklength (CBL) allocation in a nonideal reconfigurable intelligent surface (RIS)-aided ultrareliable and low-latency communication (URLLC) system. The considered scenario is a finite blocklength (FBL) regime and the problem is solved by leveraging a deep reinforcement learning (DRL) algorithm named twin-delayed deep deterministic policy gradient (TD3). First, assuming an industrial automation system, the signal-to-interference-plus-noise ratio and achievable rate in the FBL regime are identified for each actuator. Next, the joint active/passive beamforming and CBL optimization problem (OP) is formulated where the objective is to maximize the total achievable FBL rate in all actuators, subject to nonlinear amplitude response at the RIS elements, BS transmit power budget, and total available CBL. Since the formulated problem is highly nonconvex and nonlinear, we resort to employing an actor–critic policy gradient DRL algorithm based on TD3. The considered method relies on interacting RIS with the industrial automation environment by taking actions which are the phase shifts at the RIS elements, CBL variables, and BS beamforming to maximize the expected observed reward, i.e., the total FBL rate. We assess the performance loss of the system when the RIS is nonideal, i.e., with nonlinear amplitude response, and compare it with ideal RIS without impairments. The numerical results show that optimizing the RIS phase shifts, BS beamforming, and CBL variables via the TD3 method with deterministic policy outperforms conventional methods and it is highly beneficial for improving the network total FBL rate considering finite CBL size.

The need for the industrial Internet-of-Things (IIoT) and the challenges faced in the transition from traditional manufacturing to the next generation of factories is the motivation behind many research works that aim to meet the requirements of future factories. Ultra-reliable low latency communication (URLLC), known as critical machine-type communications (cMTC), is a key enabling force for future connectivity to bring about prominent opportunities in support of the IIoT. The goal of this thesis is to study different enablers for IIoT, discuss their associated challenges, research directions, and specifically focus on diversity mechanisms as a potential enabler for URLLC services in the factories of future. The first part of this thesis provides an overview of potential enablers to meet URLLC requirements. We discuss the corresponding merits and challenges of each technique, and their relevance to IIoT is highlighted. Thereafter, we survey cooperative diversity mechanisms to enable URLLC applications, and meet their corresponding requirements. The second part focuses on the performance analysis of URLLC via cooperative diversity techniques. Different performance metrics such as reliability, latency, and energy efficiency are studied, and the impact of different parameters such as coding rate, transmission power, pilot power/length, blocklength, and resource allocation under a hard deadline on system performance are examined. We aim to mathematically model and characterize the trade-offs between reliability and latency requirements. Finally, the last part of the thesis looks beyond the cooperative diversity technique to enable URLLC services for future connectivity., Tiivistelmä Tarve teolliseen esineiden Internettiin (IIoT) ja haasteet siirtymässä perinteisistä tuotantomenetelmistä seuraavan sukupolven tehtaisiin motivoivat useita tutkimustöitä löytämään ratkaisuja niiden vaatimuksiin. Erittäin luotettava pienen latenssin tiedonsiirto (URLLC), joka tunnetaan myös nimellä kriittinen konetyyppinen tietoliikenne (cMTC), on avainasemassa tulevaisuuden verkkoliittyvyydessä tarjoten huomiota herättäviä mahdollisuuksia IIoT-ratkaisuiksi. Tämän väitöskirjan tavoitteena on tutkia erilaisia IIoT:n mahdollistavia tekniikoita, käsitellä niihin liittyviä haasteita ja tutkimussuuntia sekä erityisesti keskittyä diversiteettimekanismeihin varteenotettavana URLLC-palvelujen mahdollistajana tulevaisuuden tehtaissa. Väitöstyön ensimmäinen osa sisältää yleiskatsauksen URLLC-vaatimukset täyttävistä tekniikkavaihtoehdoista. Kunkin tekniikan hyvät ja huonot puolet esitetään ja niiden IIoT-soveltuvuutta arvioidaan. Seuraavaksi tarkastellaan yhteistoiminnallisia diversiteettimenetelmiä mahdollistamaan URLLC-sovelluksia ja toteuttamaan niiden esittämiä vaatimusmäärittelyjä. Työn toinen osa keskittyy URLLC:n suorituskykyanalyysiin yhteistoiminnallisia diversiteettitekniikoita käytettäessä. Luotettavuus, latenssi ja energiatehokkuus ovat esimerkkejä monista tutkittavista suorituskykymittareista. Samoin eri parametrien, kuten koodaussuhde, lähetysteho, pilottisymbolin teho/pituus, lohkonpituus ja resurssiallokaatio, vaikutusta kovan määräajan järjestelmän suorituskykyyn tutkitaan. Työssä tähdätään matemaattiseen mallinnukseen luotettavuuden ja latenssivaatimusten välisen vuorovaikutuksen luonnehtimiseksi. Työn loppuosa tarkastelee yhteistoiminnallisten diversiteettitekniikoiden ulkopuolisia vaihtoehtoja URLLC-palveluihin tulevaisuuden verkkoliittyvyystoteutuksissa.

Radio frequency wireless energy transfer (WET) is a promising solution for powering autonomous Internet of Things (IoT) deployments. In this work, we leverage energy beamforming for powering multiple user equipments (UEs) with stringent energy harvesting (EH) demands in an indoor distributed massive multiple-input multiple-output system. Based on semi-definite programming, successive convex approximation (SCA), and maximum ratio transmission (MRT) techniques, we derive optimal and sub-optimal precoders aimed at minimizing the radio stripes’ transmit power while exploiting information of the power transfer efficiency of the EH circuits at the UEs. Moreover, we propose an analytical framework to assess and control the electromagnetic field (EMF) radiation exposure in the considered indoor scenario. Numerical results show that i) the EMF radiation exposure can be more easily controlled at higher frequencies at the cost of a higher transmit power consumption, ii) training is not a very critical factor for the considered indoor system, iii) MRT/SCA-based precoders are particularly appealing when serving a small number of UEs, thus, especially suitable for implementation in a time domain multiple access (TDMA) scheduling framework, and iv) TDMA is more efficient than spatial domain multiple access (SDMA) when serving a relatively small number of UEs. Results suggest that additional boosting performance strategies are needed to increase the overall system efficiency, thus making the technology viable in practice.

In virtualized radio access network (vRAN), the base station (BS) functions are decomposed into virtualized components that can be hosted at the centralized unit or distributed units through functional splits. Such flexibility has many benefits; however, it also requires solving the problem of finding the optimal splits of functions of the BSs in such a way that minimizes the total network cost. The underlying vRAN system is complex and precise modelling of it is not trivial. Formulating the functional split problem to minimize the cost results in a combinatorial problem that is provably NP-hard, and solving it is computationally expensive. In this paper, a constrained deep reinforcement learning (RL) approach is proposed to solve the problem with minimal assumptions about the underlying system. Since in deep RL, the action selection is the outcome of inference of a neural network, it can be done in real-time while training to update the neural networks can be done in the background. However, since the problem is combinatorial, even for a small number of functions, the action space of the RL problem becomes large. Therefore, to deal with such a large action space, a chain rule-based stochastic policy is exploited in which a long short-term memory (LSTM) network-based sequence-to-sequence model is applied to estimate the policy that is selecting the functional split actions. However, the utilized policy is still limited to an unconstrained problem, and each split decision is bounded by vRAN’s constraint requirements. Hence, a constrained policy gradient method is leveraged to train and guide the policy toward constraint satisfaction. Further, a search strategy by greedy decoding or temperature sampling is utilized to improve the optimality performance at the test time. Simulations are performed to evaluate the performance of the proposed solution using synthetic and real network datasets. Our numerical results show that the proposed RL solution architecture

An reconfigurable intelligent surface (RIS) can be used to establish line-of-sight (LoS) communication when the direct path is compromised, which is a common occurrence in a millimeter wave (mmWave) network. In this paper, we focus on the uplink channel estimation of a such network. We formu-late this as a sparse signal recovery problem, by discretizing the angle of arrivals (AoAs) at the base station (BS). On-grid and off-grid AoAs are considered separately. In the on-grid case, we propose an algorithm to estimate the direct and RIS channels. Neural networks trained based on supervised learning is used to estimate the residual angles in the off-grid case, and the AoAs in both cases. Numerical results show the performance gains of the proposed algorithms in both cases.

One of the key benefits of virtualized radio access networks (vRANs) is network management flexibility. However, this versatility raises previously-unseen network management challenges. In this paper, a learning-based zero-touch vRAN orchestration framework (LOFV) is proposed to jointly select the functional splits and allocate the virtualized resources to minimize the long-term management cost. First, testbed measurements of the behaviour between the users’ demand and the virtualized resource utilization are collected using a centralized RAN system. The collected data reveals that there are non-linear and non-monotonic relationships between demand and resource utilization. Then, a comprehensive cost model is proposed that takes resource overprovisioning, declined demand, instantiation and reconfiguration into account. Moreover, the proposed cost model also captures different routing and computing costs for each split. Motivated by our measurement insights and cost model, LOFV is developed using a model-free reinforcement learning paradigm. The proposed solution is constructed from a combination of deep Q-learning and a regression-based neural network that maps the network state and users’ demand into split and resource control decisions. Our numerical evaluations show that LOFV can offer cost savings by up to 69% of the optimal static policy and 45% of the optimal fully dynamic policy.

Radio-frequency (RF) wireless energy transfer (WET) is a key technology that may allow seamlessly powering future massive low-energy Internet of Things (IoT) networks. To enable efficient massive WET, channel state information (CSI)-limited/free multiantenna transmit schemes have been recently proposed in the literature. The idea is to reduce/null the energy costs to be paid by energy harvesting (EH) IoT nodes from participating in large-scale time/power-consuming CSI training, but still enable some transmit spatial gains. In this article, we take another step forward by proposing a novel CSI-free rotary antenna beamforming (RAB) WET scheme that outperforms all state-of-the-art CSI-free schemes in a scenario, where a power beacon (PB) equipped with a uniform linear array (ULA) powers a large set of surrounding EH IoT devices. RAB uses a properly designed CSI-free beamformer combined with a continuous or periodic rotation of the ULA at the PB to provide average EH gains that scale as \(0.85\sqrt{M}\), where \(M\) is the number of PB’s antenna elements. Moreover, a rotation-specific power control mechanism was proposed to: 1) fairly optimize the WET process if devices’ positioning information is available and/or 2) avoid hazards to human health in terms of specific absorption rate (SAR), which is an RF exposure metric that quantifies the absorbed power in a unit mass of human tissue. We show that RAB performance even approaches quickly (or surpasses, for scenarios with a sufficiently large number of EH devices, or when using the proposed power control) the performance of a traditional full-CSI-based transmit scheme, and it is also less sensitive to SAR constraints. Finally, we discuss important practicalities related to RAB such as its robustness against non line-of-sight (LOS) conditions compared to other CSI-free WET schemes, and its generalizability to scenarios where the PB uses other than a ULA topology.

Various industrial Internet of Things applications demand execution periods throughout which no communication failure is tolerated. However, the classical understanding of reliability in the context of ultra-reliable low-latency communication (URLLC) does not reflect on the time-varying characteristics of the wireless channel. In this article, we introduce a novel mission reliability and mission effective capacity metric that takes these phenomena medium into account, while specifically studying multiconnectivity (MC)-enabled industrial radio systems. We assume uplink short packet transmission with no channel state information at URLLC user (the transmitter) and sporadic traffic arrival. Moreover, we leverage the existing framework of dependability theory and provide closed-form expressions (CFEs) for the mission reliability of the MC system using the maximal-ratio combining scheme. We do so by utilizing the mean time to first failure, which is the expected time of failure occurring for the first time. Moreover, we also derive exact CFEs for second-order statistics, such as level crossing rate and average fade duration, showing how fades are distributed in fading channels with respect to time. Furthermore, the design throughput maximization problem under the mission reliability constraint is solved numerically through the cross-entropy method.

Deep learning (DL) driven proactive resource allocation (RA) is a promising approach for the efficient management of network resources. However, DL models typically have a limitation that they do not capture the uncertainty due to the arrival of new unseen samples with a distribution different than the data distribution available at DL model-training time, leading to wrong resource usage predictions. To address this, we propose a confidence aware DL solution for the robust and reliable predictions of wireless channel utilization (CU) in shared spectrum bands. We utilize an encoder-decoder based Bayesian DL model to generate prediction intervals which capture the uncertainties in wireless CU. We use the CU predictions to design a novel metric score which in turn is utilized to make an adaptive RA algorithm. We show that a DL model capturing uncertainty in CU can achieve higher data rates for a wireless network. Both DL driven predictions and RA models are tested using synthetic data as well as real CU data collected in the University of Oulu. Using analytical and simulations results, we also study the stability of the proposed RA algorithm and show that it converges to a Nash equilibrium (NE). Our results reveal that the proposed algorithm converges to an NE under 2\(N\) iterations where \(N\) is the number of network access points.

In this paper, we evaluate the Effective Energy Efficiency (EEE) and propose delay-outage aware resource allocation strategies for energy-limited IoT (Internet of Things) devices under the finite blocklength (FBL) regime. The EEE is a cross-layer model, measured by the ratio of Effective Capacity to the total consumed power. To maximize the EEE, there is a need to optimize transmission parameters such as transmission power and rate efficiently. Whereas it is quite complex to study the impact of transmission power, or rate alone, the complexity is aggravated by the simultaneous consideration of both variables. Hence, we formulate power allocation (PA) and rate allocation (RA) optimization problems individually and jointly to maximize EEE. Furthermore, we investigate the performance of the EEE under constant and random arrivals, where statistical QoS constraints are imposed on buffer overflow probability. Using effective bandwidth and effective capacity theories, we determine the arrival rate and the required service rate that satisfy the QoS constraints. After that, we compare the performance of different iterative algorithms such as Dinkelbach’s and Cross Entropy, which guarantee the convergence for the optimal solution. By numerical analysis, the influence of source characteristics, fixed transmission rate, error probability, coding blocklength, and QoS constraints on the throughput are identified. Our analysis reveals that the joint PA and RA is the optimal resources allocation strategy for maximizing the EEE in the presence of constant and random data arrivals. Finally, the results illustrate that the modified Dinkelbach’s algorithm has high performance and low complexity compared to others.

We study the practical phase shift design in a non-ideal reconfigurable intelligent surface (RIS)-aided ultra-reliable and low-latency communication (URLLC) system under finite blocklength (FBL) regime by leveraging a novel deep reinforcement learning (DRL) algorithm named as twin-delayed deep deterministic policy gradient (TD3). First, assuming industrial automation system with multiple actuators, the signal-to-interference-plus-noise ratio (SINR) and achievable rate in FBL regime are identified for each actuator in terms of the phase shift configuration matrix at the RIS. The channel state information (CSI) variations due to feedback delay are also considered that result in channel coefficients’ obsolescence. Then, the problem framework is proposed where the objective is to maximize the total achievable FBL rate in all ACs, subject to the practical phase shift constraint at the RIS elements. Since the problem is intractable to solve using conventional optimization methods, we resort to employing an actor-critic policy gradient DRL algorithm based on TD3, which relies on interacting RIS with FA environment by taking actions which are the phase shifts at the RIS elements, to maximize the expected observed reward, which is defined as the total FBL rate. The numerical results show that optimizing the practical phase shifts in the RIS via the proposed TD3 method is highly beneficial to improve the network total FBL rate in comparison with typical DRL methods.

Many emerging Internet of Things (IoT) applications rely on information collected by sensor nodes where the freshness of information is an important criterion. Age of Information (AoI) is a metric that quantifies information timeliness, i.e., the freshness of the received information or status update. This work considers a setup of deployed sensors in an IoT network, where multiple unmanned aerial vehicles (UAVs) serve as mobile relay nodes between the sensors and the base station. We formulate an optimization problem to jointly plan the UAVs’ trajectory, while minimizing the AoI of the received messages. This ensures that the received information at the base station is as fresh as possible. The complex optimization problem is efficiently solved using a deep reinforcement learning (DRL) algorithm. In particular, we propose a deep Q-network, which works as a function approximation to estimate the state-action value function. The proposed scheme is quick to converge and results in a lower AoI than the random walk scheme. Our proposed algorithm reduces the average age by approximately 25% and requires down to 50% less energy when compared to the baseline scheme.

In this paper, a multi-objective optimization problem (MOOP) is proposed for maximizing the achievable finite blocklength (FBL) rate while minimizing the utilized channel blocklengths (CBLs) in a reconfigurable intelligent surface (RIS)-assisted short packet communication system. The formulated MOOP has two objective functions namely maximizing the total FBL rate with a target error probability, and minimizing the total utilized CBLs which is directly proportional to the transmission duration. The considered MOOP variables are the base station (BS) transmit power, number of CBLs, and passive beamforming at the RIS. Since the proposed non-convex problem is intractable to solve, the Tchebyshev method is invoked to transform it into a single-objective OP, then the alternating optimization (AO) technique is employed to iteratively obtain optimized parameters in three main sub-problems. The numerical results show a fundamental trade-off between maximizing the achievable rate in the FBL regime and reducing the transmission duration. Also, the applicability of RIS technology is emphasized in reducing the utilized CBLs while increasing the achievable rate significantly.

Ambient radio frequency (RF) energy harvesting (EH) technology is key to realize self-sustainable, always-on, low-power, massive Internet of Things networks. Typically, rigid (non-adaptable to channel fluctuations) multi-antenna receive architectures are proposed to support reliable EH operation. Herein, we introduce a dynamic RF combining architecture for ambient RF EH use cases, and exemplify the attainable performance gains via three simple phase shifts’ exploration mechanisms, namely, brute force (BF), sequential testing (ST) and codebook based (CB). Among the proposed mechanisms, BF demands the highest power consumption, while CB requires the highest-resolution phase shifters, thus tipping the scales in favor of ST. Finally, we show that the performance gains of ST over a rigid RF combining scheme increase with the number of receive antennas and energy transmitters’ deployment density.

This dissertation presents cooperative beamformer techniques and possible practical implementations to mitigate the sophisticated interference scenarios in dynamic time-division-duplexing (TDD) systems and integrated access and backhaul (IAB) networks. Greater emphasis is placed on the distributed resource allocation to facilitate simultaneous uplink (UL) and downlink (DL) data transmission by employing multiple-input multiple-output (MIMO) beamformer techniques and bi-directional signalling schemes with different objectives to optimize network utilities while acknowledging important practical considerations and future traffic demand. The first half of this thesis focuses on dynamic TDD systems, in which available resources per cell can be freely allocated to either UL or DL depending on the instantaneous traffic demand. Hence, complicated UL-DL and DL-UL interference scenarios arise due to simultaneous UL and DL data transmission in adjacent cells. Primary attention is given to mitigation of catastrophic interference exposure in dynamic TDD systems by employing MIMO based decentralized iterative beamforming techniques with traffic-aware network optimization objectives, supported with minimal information exchange among the coordinated base stations (BSs) and user-equipments (UEs). Bi-directional forward-backward training via spatially precoded over-the-air pilot signalling is used to facilitate coordinated beamforming. Novel bi-directional beamformer training strategies and methods for direct estimation (DE) of the stream specific beamformers are developed for each intermediate beamformer update, using overlapping and non-orthogonal pilots. The latter half of this thesis considers an IAB network consisting of a BS, IAB-relays, and UEs. Both BS and IAB-relay provide access to UEs while BS and relays exchange UEs data via a wireless in-band backhaul using the same frequency-time resources shared with access links. A flexible TDD-based IAB network is considere, Tiivistelmä Tämä väitöskirja esittelee yhteistoiminnallisia keilanmuodostustekniikoita ja mahdollisia käytännön toteutuksia monimutkaisten häiriöskenaarioiden lieventämiseksi dynaamisissa aikajako-dupleksointijärjestelmissä (TDD) ja integroiduissa pääsy- ja backhaul-verkoissa (IAB). Enemmän painoarvoa on annettu hajautettuun resurssien allokoi, joka mahdollistaa samanaikaisen uplink (UL) ja downlink (DL) tiedonsiirron käyttämällä moniantennijärjestelmän (MIMO) keilanmuodostustekniikat ja kaksisuuntaiset signalointimenetelmät, joilla on eri tavoitteet verkon apuohjelmien optimoimiseksi, samalla kun otetaan huomioon tärkeät käytännön näkökohdat ja tuleva dataliikenteen kysyntä. Tämän opinnäytetyön ensimmäinen puolisko keskittyy dynaamisiin TDD-järjestelmiin, joissa käytettävissä olevat resurssit solua kohden voidaan allokoida vapaasti joko UL:lle tai DL:lle riippuen hetkellisestä liikennetarpeesta. Tästä syystä syntyy monimutkaisia UL-DL- ja DL-UL-häiriöskenaarioita, jotka johtuvat samanaikaisesta UL- ja DL-tiedonsiirrosta viereisissä soluissa. Ensisijainen huomio kiinnitetään katastrofaalisen häiriöaltistuksen lieventämiseen dynaamisissa TDD-järjestelmissä käyttämällä MIMO-pohjaista hajautettuja iteratiivisia keilanmuodostustekniikoita liikennetietoisilla verkon optimointitavoitteilla, joita tuetaan minimaalisella tiedonvaihdolla koordinoitujen tukiasemien (BSs) ja käyttäjälaitteiden välillä (UEs). Kaksisuuntaista eteenpäin-taakse-koulutusta spatiaalisesti esikoodatun over-the-air-pilottisignaloinnin avulla käytetään koordinoidun säteenmuodostuksen helpottamiseksi. Uusia kaksisuuntaisia keilanmuodostajien koulutusstrategioita ja menetelmiä keilanmuodostajien suoraa estimointia (DE) varten kehitetään jokaista välikeilanmuodostajan päivitystä varten käyttämällä päällekkäisiä ja ei-ortogonaalisia pilotteja. Tämän väitöskirjan jälkimmäinen puolisko käsittelee IAB-verkkoa, joka koostuu BS:stä, IAB-releistä ja UE:ista. Sekä BS että IAB-rele tarjoavat pääsyn UE:ihin, kun ta

This paper introduces a new interference-aware user association (UA) scheme for a multi-base station (BS) wireless network in which intelligent reflecting surfaces (IRSs) are leveraged to improve each multi-antenna BS’s coverage region and mitigate the vulnerability to non-line of sight paths. We aim to maximize the total network downlink achievable rate by jointly optimizing the reflective phase shifters at IRSs while associating mobile users (MUs) to BSs, which is an intractable non-convex problem. An alternating optimization-based algorithm based on solving two sub-problems, i.e., one for UA and one for reflective phase shift optimization, is proposed to tackle the non-convex problem. The proposed algorithm optimizes phase shifters at IRS through fractional programming techniques, and the UA is solved by successive convex approximation (SCA). Simulation results show that the proposed algorithm significantly improves the total network achievable rate compared to heuristic methods, e.g., matching game.

Future industrial applications will encompass compelling new use cases requiring stringent performance guarantees over multiple key performance indicators (KPI) such as reliability, dependability, latency, time synchronization, security, etc. Achieving such stringent and diverse service requirements necessitates the design of a special-purpose Industrial Internet of Things (IIoT) network comprising a multitude of specialized functionalities and technological enablers. This article proposes an innovative architecture for such a special-purpose 6 G IIoT network incorporating seven functional building blocks categorized into: special-purpose functionalities and enabling technologies. The former consists of Wireless Environment Control, Traffic/Channel Prediction, Proactive Resource Management and End-to-End Optimization functions; whereas the latter includes Synchronization and Coordination, Machine Learning and Artificial Intelligence Algorithms, and Auxiliary Functions. The proposed architecture aims at providing a resource-efficient and holistic solution for the complex and dynamically challenging requirements imposed by future 6 G industrial use cases. Selected test scenarios are provided and assessed to illustrate cross-functional collaboration and demonstrate the applicability of the proposed architecture in a wireless IIoT network.

Ultra-Reliable Low Latency Communication (URLLC) is a newly introduced service class targeting emerging Internet-of-Things (IoT) application scenarios. This paper assumes an interference-limited Fog Radio Access Network (F-RAN) setup composed of multiple Remote Radio Heads (RRHs) equipped with multiple antennas serving single-antenna users. F-RAN facilitates collaborative solutions while reducing delay by pushing the network capabilities beyond the edge. By leveraging diversity, RRHs may cooperate through silencing, reducing interference, or joint transmission strategies such as maximal ratio transmission. We derive closed-form outage probability expressions and attain their diversity gain. We validate the derived analytical results through extensive numerical simulations. Furthermore, we propose a mini-slots-based scheduling framework to serve URLLC users within their fixed latency budget. In an interference-limited regime with the proposed scheduling framework, we show that a performance gain is superior when RRHs cooperate compared to when they do not. We briefly discuss the cost of reliability, i.e., the impact on the system’s average sum throughput under cooperation. Moreover, numerical results verify that cooperating transmission schemes boost transmission reliability with a significantly improved latency performance at the cost of reduced system’s average sum throughput.

Effective capacity (EC) defines the maximum communication rate subject to a specific delay constraint, while effective energy efficiency (EEE) indicates the ratio between EC and power consumption. We analyze the EEE of ultrareliable networks operating in the finite-blocklength regime. We obtain a closed-form approximation for the EEE in quasistatic Nakagami- m (and Rayleigh as subcase) fading channels as a function of power, error probability, and latency. Furthermore, we characterize the quality-of-service constrained EEE maximization problem for different power consumption models, which shows a significant difference between finite and infinite-blocklength coding with respect to EEE and optimal power allocation strategy. As asserted in the literature, achieving ultrareliability using one transmission consumes a huge amount of power, which is not applicable for energy limited Internet-of-Things devices. In this context, accounting for empty buffer probability in machine-type communication (MTC) and extending the maximum delay tolerance jointly enhances the EEE and allows for adaptive retransmission of faulty packets. Our analysis reveals that obtaining the optimum error probability for each transmission by minimizing the nonempty buffer probability approaches EEE optimality, while being analytically tractable via Dinkelbach’s algorithm. Furthermore, the results illustrate the power saving and the significant EEE gain attained by applying adaptive retransmission protocols, while sacrificing a limited increase in latency.