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Unmanned aerial vehicles (UAVs) are the emerging vital components of millimeter wave (mmWave) wireless systems. Accurate beam alignment is essential for efficient beam based mmWave communications of UAVs with base stations (BSs). Conventional beam sweeping approaches often have large overhead due to the high mobility and autonomous operation of UAVs. Learning-based approaches greatly reduce the overhead by leveraging UAV data, like position to identify optimal beam directions. In this paper, we propose a deep Q-Network(DQN)-based framework for uplink UAV-BS beam alignment where the UAV hovers around 5G new radio (NR) BS coverage area, with varying channel conditions. The proposed learning framework uses the location information and maximize the beamforming gain upon every communication request from UAV inside the multi-location environment. We compare the proposed framework against multi-armed bandit (MAB)-based and exhaustive approaches, respectively and then analyse its training performance over different coverage area requirements, antenna configurations and channel conditions. Our results show that the proposed framework converge faster than the MAB-based approach and comparable to traditional exhaustive approach in an online manner under real-time conditions. Moreover, this approach can be further enhanced to predict the optimal beams for unvisited UAV locations inside the coverage using correlation from neighbouring grid locations.

The provision of accurate localization is an increasingly important feature of wireless networks. To this end, a reliable distinction between line-of-sight (LOS) and non-LOS (NLOS) radio links is necessary to avoid degenerative localization estimation biases. Interestingly, LOS and NLOS transmissions affect the polarization of the received signals differently. In this work, we leverage this phenomenon to propose a threshold-based LOS/NLOS classifier exploiting weighted differential carrier phase measurements over a single link with different polarization configurations. Operation in either full or limited polarization diversity systems is possible. We develop a framework for assessing the performance of the proposed classifier, and show through simulations the performance impact of the reflecting materials in NLOS scenarios. For instance, the classifier is far more efficient in NLOS scenarios with wooden reflectors than in those with metallic reflectors. Numerical results evince the potential performance gains from exploiting full polarization diversity, properly weighting the differential carrier phase measurements, and using multi-carrier/tone transmissions. Finally, we show that the optimum decision threshold is inversely proportional to the path power gain in dB, while it does not depend significantly on the material of potential NLOS reflectors.

In this paper, we investigate a transmitter and receiver design for a single-user massive SIMO (single-input multiple-output) system with 1-bit analog-to-digital converters (ADCs) at the base station (BS), where the user adopts higher-order modulation, e.g., 16-quadrature amplitude modulation (16-QAM), for the data transmission. For the channel estimation and the signal detection, linear least-squares (LS) estimation and maximum ratio combining (MRC) are respectively employed. In this context, we first introduce closed-form formulas for the mean of the estimated symbols and for the correlation matrix between their real and imaginary parts considering the effect of 1-bit quantization. The study of the distribution of the estimated symbols indicates that, in presence of 1-bit ADCs, the conventional 16-QAM detector and the typical square 16-QAM modulation are not adequate. In light of this, we propose three novel symbol detectors and re-design the 16-QAM modulation in order to improve the symbol error rate (SER). Furthermore, the upper bound on the SER is analyzed based on the pair-wise error probability and the boundary equation between two regions is also studied. Through numerical results, the proposed framework, i.e., the symbol detector and the transmit constellation design, shows a significant enhancement in the SER performance against the conventional detector and the typical square 16-QAM modulation.

Power splitting (PS) based simultaneous wireless information and power transfer (SWIPT) is considered in a multi-user multiple-input-single-output broadcast scenario. Specifically, we focus on jointly configuring the transmit beamforming vectors and receive PS ratios to minimize the total transmit energy of the base station under the user-specific latency and energy harvesting (EH) requirements. The battery depletion phenomenon is avoided by preemptively incorporating information regarding the receivers’ battery state and EH fluctuations into the resource allocation design. The resulting time-average sum-power minimization problem is temporally correlated, non-convex (including mutually coupled latency-battery queue dynamics), and in general intractable. We use the Lyapunov optimization framework and derive a dynamic control algorithm to transform the original problem into a sequence of per-time-slot deterministic and independent subproblems. The latter are then solved via two alternative approaches: i) semidefinite relaxation combined with fractional programming, and ii) successive convex approximation. Furthermore, we design a low-complexity closed-form iterative algorithm exploiting the Karush-Kuhn-Tucker optimality conditions for a specific scenario with delay bounded batteryless receivers. Numerical results provide insights on the robustness of the proposed designs to realize an energy-efficient SWIPT system while ensuring latency and EH requirements in a time dynamic network.

The next evolutionary step in human-computer interfaces will bring forward immersive digital experiences that submerge users in a 3D world while allowing them to interact with virtual or twin objects. Accordingly, various collaborative extended reality (XR) applications are expected to emerge, imposing stringent performance requirements on the underlying wireless connectivity infrastructure. In this paper, we examine how novel multi-antenna coded caching (CC) techniques can facilitate high-rate low-latency communications and improve users’ quality of experience (QoE) in our envisioned multi-user XR scenario. Specifically, we discuss how these techniques make it possible to prioritize the content relevant to wireless bottleneck areas while enabling the cumulative cache memory of the users to be utilized as an additional communication resource. In this regard, we first explore recent advancements in multi-antenna CC that facilitate the efficient use of distributed in-device memory resources. Then, we review how XR application requirements are addressed within the third-generation partnership project (3GPP) framework and how our envisioned XR scenario relates to the foreseen use cases. Finally, we identify new challenges arising from integrating CC techniques into multi-user XR scenarios and propose novel solutions to address them in practice.

We provide new analytical results on the uplink data detection in massive multiple-input multiple-output systems with 1-bit analog-to-digital converters. The statistical properties of the soft-estimated symbols (i.e., after linear combining and prior to the data detection process) have been previously characterized only for a single user equipment (UE) and uncorrelated Rayleigh fading. In this paper, we consider a multi-UE setting with correlated Rayleigh fading, where the soft-estimated symbols are obtained by means of maximum ratio combining based on imperfectly estimated channels. We derive a closed-form expression of the expected value of the soft-estimated symbols, which allows to understand the impact of the specific data symbols transmitted by the interfering UEs. Building on this result, we design efficient data detection strategies based on the minimum distance criterion, which are compared in terms of symbol error rate and complexity.

A device-to-device (D2D) aided multi-antenna coded caching scheme is proposed to improve the average delivery rate and reduce the downlink (DL) beamforming complexity. Novel beamforming and resource allocation schemes are proposed where local data exchange among nearby users is exploited. The transmission is split into two phases: local D2D content exchange and DL transmission. In the D2D phase, subsets of users are selected to share content with the adjacent users directly. In this regard, a low complexity D2D mode selection algorithm is proposed to find the appropriate set of users for the D2D phase with comparable performance to the optimal exhaustive search. During the DL phase, the base station multicasts the remaining data requested by all the users. We identify scenarios and conditions where D2D transmission can reduce the delivery time. Furthermore, we demonstrate how adding the new D2D phase to the DL-only scenario can significantly reduce the beamformer design complexity in the DL phase. The results further highlight that by partly delivering requested data in the D2D phase, the transmission rate can be boosted due to more efficient use of resources during the subsequent DL phase. As a result, the overall content delivery performance is greatly enhanced, especially in the finite signal-to-noise (SNR) regime.

We consider unmanned aerial vehicle (UAV)-enabled wireless systems where downlink communications between a multi-antenna UAV and multiple users are assisted by a hybrid active-passive reconfigurable intelligent surface (RIS). We aim at a fairness design of two typical UAV-enabled networks, namely the static-UAV network where the UAV is deployed at a fixed location to serve all users at the same time, and the mobile-UAV network which employs the time division multiple access protocol. In both networks, our goal is to maximize the minimum rate among users through jointly optimizing the UAV’s location/trajectory, transmit beamformer, and RIS coefficients. The resulting problems are highly nonconvex due to a strong coupling between the involved variables. We develop efficient algorithms based on block coordinate ascend and successive convex approximation to effectively solve these problems in an iterative manner. In particular, in the optimization of the mobile-UAV network, closed-form solutions to the transmit beamformer and RIS passive coefficients are derived. Numerical results show that a hybrid RIS equipped with only 4 active elements and a power budget of 0 dBm offers an improvement of 38% — 63% in minimum rate, while that achieved by a passive RIS is only about 15%, with the same total number of elements.

We propose a novel message passing de-quantization (MPDQ) algorithm for low-complexity uplink signal detection in mmWave large multi-user multi-input multi-output (MU-MIMO) systems with low-resolution analog-to-digital converters (ADCs) suffering from severe quantization errors. The proposed method consists of a de-quantization (DQ) step based on the Bussgang theorem and a Bayesian multi-user detection (MUD) via Gaussian belief propagation (GaBP), which detects the uplink signal while compensating for the quantized signal distortion. The efficacy is demonstrated by simulation results, which are shown to significantly outperform the current state-of-the-art (SotA) detection designed by Bussgang minimum mean square error (BMMSE) and generalized approximate message passing (GAMP) frameworks in 1-bit quantization, and approach the matched filter bound (MFB) performance.

We consider a novel hybrid active-passive reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV) air-ground communications system. Unlike the conventional passive RIS, the hybrid RIS is equipped with a few active elements to not only reflect but also amplify the incident signals for significant performance improvement. Towards a fairness design, our goal is to maximize the minimum rate among users through jointly optimizing the location and power allocation of the UAV and the RIS reflecting/amplifying coefficients. The formulated optimization problem is nonconvex and challenging, which is efficiently solved via block coordinate descend and successive convex approximation. Our numerical results show that a hybrid RIS requires only 4 active elements and a power budget of 0 dBm to achieve an improvement of 52.08% in the minimum rate, while that achieved by a conventional passive RIS with the same total number of elements is only 18.06%.

Multi-antenna coded caching (CC) techniques are considered viable options for achieving higher data rates in future networks, especially for the prominent use case of multimedia-driven applications. However, despite their information-theoretic analyses, which are thoroughly studied in the literature, the research on the finite-SNR performance of multi-antenna CC techniques is not yet mature. In this paper, we try bridging this gap by breaking down, categorizing, and studying the effect of six crucial parameters affecting the finite-SNR performance of multi-antenna CC schemes. We also investigate the interaction of different parameters and clarify how they could affect the implementation complexity in terms of the necessary computation and subpacketization. Theoretical discussions are followed and verified by numerical analysis.

We consider multi-group multicast precoding designs for cell-free massive multiple-input multiple-output (MIMO) systems. To optimize the transmit and receive beamforming strategies, we focus on minimizing the sum of the maximum mean squared errors (MSEs) over the multicast groups, which is then approximated with the sum MSE to simplify the computation and signaling. We adopt an iterative bi-directional training scheme with uplink and downlink precoded pilots to cooperatively design the multi-group multicast precoders at each base station and the combiners at each user equipment in a distributed fashion. An additional group-specific uplink training resource is introduced, which entirely eliminates the need for backhaul signaling for channel state information (CSI) exchange. We also propose a simpler distributed precoding design based solely on group-specific pilots, which can be useful in the case of scarce training resources. Numerical results show that the proposed distributed methods greatly outperform conventional cell-free massive MIMO precoding designs that rely solely on local CSI.

This paper studies the performance of wireless coded caching over multiple-input and single-output (MISO) channels in a finite signal-to-noise power ratio (SNR) region when every user has a different cache memory size. We first propose multicast beamforming for the network with the conventional coded caching based on quadratic transform (QT) and then point out the non-optimality of the caching scheme when the spatial degree of freedom (DoF) is exploited. We hence formulate a new optimization problem to enhance the caching gain by minimizing the difference between the generated codewords. Numerical results confirm the non-optimality of the conventional coded caching in terms of the average transmission rate and the improvement of our proposed caching.

Efficient usage of in-device storage and computation capabilities are key solutions to support data-intensive applications such as immersive digital experiences. This paper proposes a location-dependent multi-antenna coded caching - based content delivery scheme tailored specifically for wireless immersive viewing applications. First, a novel memory allocation process incentivizes the content relevant to the identified wireless bottleneck areas. This enables a trade-off between local and global caching gains and results in unequal fractions of location-dependent multimedia content cached by each user. Then, a novel packet generation process is carried out during the subsequent delivery phase, given the asymmetric cache placement. During this phase, the number of packets transmitted to each user is the same, while the sizes of the packets are proportional to the corresponding location-dependent cache ratios. In this regard, each user is served with location-specific content using joint multicast beamforming and a multi-rate modulation scheme that simultaneously benefits from global caching and spatial multiplexing gains. Numerical experiments and mathematical analysis demonstrate significant performance gains compared to the state-of-the-art.

We consider a multi-user multiple-input single-output (MISO) communications system which is assisted by a hybrid active-passive reconfigurable intelligent surface (RIS). Unlike conventional passive RISs, hybrid RIS is equipped with a few active elements with the ability to reflect and amplify incident signals to significantly improve the system performance. Towards a fairness-oriented design, we maximize the minimum rate among all users through jointly optimizing the transmit beamforming vectors and RIS reflecting/amplifying coefficients. Combining tools from block coordinate ascent and successive convex approximation, the challenging nonconvex problem is efficiently solved by a low-complexity iterative algorithm. The numerical results show that a hybrid RIS with 4 active elements out of a total of 50 elements with a power budget of −1 dBm offers an improvement of up to 80% to the considered system, while that achieved by a fully passive RIS is only 27%.

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.

Multi-antenna coded caching is known to combine a global caching gain that is proportional to the cumulative cache size found across the network, with an additional spatial multiplexing gain that stems from using multiple transmitting antennas. However, a closer look reveals two severe bottlenecks; the well-known exponential subpacketization bottleneck that dramatically reduces performance when the communicated file sizes are finite, and the considerable optimization complexity of beamforming multicast messages when the SNR is finite. We here present an entirely novel caching scheme, termed cyclic multi-antenna coded caching, whose unique structure allows for the resolution of the above bottlenecks in the crucial regime of many transmit antennas. For this regime, where the multiplexing gain can exceed the coding gain, our new algorithm is the first to achieve the exact one-shot linear optimal DoF with a subpacketization complexity that scales only linearly with the number of users, and the first to benefit from a multicasting structure that allows for exploiting uplink-downlink duality in order to yield optimized beamformers ultra-fast. In the end, our novel solution provides excellent performance for networks with finite SNR, finite file sizes, and many users.

In large multi-user multi-input multi-output systems, the computational cost and circuit scale of base stations (BSs) are effectively reduced using two-stage signal processing consisting of a slow varying outer beamformer (OBF) based on long-term channel statistics and group-specific multi-user detection for instantaneous channel variations. However, the dimensionality reduction of the group-specific beam-domain channel based on the OBF causes significant performance degradation in the subsequent spatial-filtering detection. To compensate for this drawback, this paper introduces a novel layered belief propagation (BP) detector with a concatenated structure of beam- and antenna-domain BP layers for post-stage OBF processing. The proposed detector is designed for improving the convergence of iterative detection by suppressing intra- and inter-group interference in stages. The layered structure provides the advantages of both beam and antenna domains while maintaining low signal-processing complexity. Numerical results show the validity of our proposed method in terms of the bit error rate performance in both the uncoded and coded cases and the computational complexity.

In this paper we revisit the long-standing problem of peak-to-average power ratio minimization in MIMO-OFDM systems, with a new angle of approach on a well-known scheme. Utilizing the principles of tone reservation, we place dummy symbols, i.e., complex coefficients, on unused space-frequency resources with the aim of jointly minimizing the transmit signal PAPR and the self-power consumption of the dummy symbols. To solve this joint minimization, we propose three different algorithms exhibiting varying degrees of computational complexity and PAPR reduction performance. Our proposed framework utilizes the strict PAPR expression, i.e., we take into account the average transmit power of the antenna, to simultaneously reduce the PAPR on all antennas while keeping the self-power consumption of the scheme minimal. Our simulation results show that this optimization objective provides better worst-case PAPR reduction and dummy symbol power consumption performance compared to the peak power minimization objective widely utilized in the tone reservation literature. Finally, we propose a novel take on a well-known block-diagonalization algorithm by exploiting knowledge on dummy symbol allocations, resulting in high-gain data streams in downlink transmission.

The sensitivity of the millimeter-wave (mmWave) radio channel to blockages is a fundamental challenge in achieving low-latency and highly-reliable connectivity. In this paper, we explore the viability of using Coordinated Multi-Point (CoMP) transmission for delay bounded and reliable mmWave systems. We propose a blockage-aware framework for the sum-power minimization problem under the user-specific latency requirements in time dynamic mobile access networks. We use the Lyapunov optimization approach and provide a dynamic control algorithm, which transforms a time-average stochastic problem into a sequence of deterministic subproblems. A robust beamformer design is then proposed by exploiting the queue backlogs and channel information, that efficiently allocates the required resources, by proactively tuning the CoMP subsets from the available remote radio units (RRUs), according to the instantaneous needs of the users. Further, to adapt to the uncertainties of the mmWave channel, we consider a pessimistic estimate of the rates over link blockage combinations across the CoMP serving set. Moreover, after the relaxation of coupled and non-convex constraints via the Fractional Program (FP) techniques, a low-complexity closed-form iterative algorithm is provided by solving a system of Karush-Kuhn-Tucker (KKT) optimality conditions. The simulation results manifest that, in the presence of random link blockages, the proposed methods outperform the baseline scenarios and provide power-efficient, highly-reliable, and low-latency mmWave communication.

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

Millimeter wave (mmWave) and sub-terahertz (sub-THz) communication systems rely mainly on the availability of dominant links between the transmitters and receivers. The sensitivity of high-frequency radio links to random blockages, however, challenges the achievable positioning, reliability, and latency requirements. Moreover, the provision of a sustainable and reliable energy source in mobile devices has to be addressed. We explore the viability of using Coordinated Multi-Point (CoMP) schemes, which facilitate multi-user processing across spatially distributed remote radio units (RRUs), to ensure accurate positioning and latency-constrained reliable mmWave communication, even if one or more dominant links are blocked. We design a blockage-aware algorithm for the weighted sum-rate maximization problem in mmWave based system. A robust downlink beamformer design, with the parallel beamformer processing across distributed RRUs, is proposed by exploiting the spatial macro-diversity and a pessimistic estimate of rates over potential link blockage combinations. This problem is extended by considering the user-specific latency requirements in a dynamic access network for the sum-power minimization objective. The time-average stochastic problem is transformed into a sequence of deterministic and independent subproblems using the Lyapunov optimization, and a dynamic control algorithm is then proposed, which enables an efficient implementation of low-complexity closed-form iterative beamformer design. Furthermore, by exploiting the queue backlogs and channel information, we propose a proactive and dynamic selection of the serving set combinations of CoMP RRUs. In the simultaneous wireless information and power transfer (SWIPT) systems, we consider the interdependence of latency-battery queue dynamics, and propose the joint beamforming and power splitting optimization to minimize the sum-power of the transmitter under user-specific latency and energy harvesting requir, Tiivistelmä Millimetriaaltojen (mmWave) ja ali-terahertzin (sub-THz) tiedonsiirto pohjautuu pääasiallisesti lähettimien ja vastaanottimien välisiin dominantteihin linkkeihin. Tästä johtuen korkeataajuuksisten radiolinkkien herkkyys satunnaisille esteille tekee paikannuksen, luotettavuuden ja latenssin mahdolliset vaatimukset haastaviksi. Tämän lisäksi mobiililaitteiden kestävä ja luotettava energiansaanti täytyy ottaa huomioon. Työssä tutkitaan koordinoitujen monipistelähetysmenetelmiä (CoMP), mitkä mahdollistavat monen käyttäjän prosessoinnin tilatasossa hajautuneiden etäradioyksiköiden (RRU) yli. Tämä varmistaa tarkan paikannuksen ja latenssirajotteisen luotettavan mmWave-tiedonsiirron, vaikka yksi tai useampi dominanteista linkeistä olisi katveessa. Työssä suunnitellaan esteet huomioivan algoritmin painotetun summatiedonsiirtonopeuden maksimointiin mmWave-tiedonsiirtoon perustuen monen yhden antennin käyttäjän. Työssä tutkitaan robustia alalinkin rinnakkaista keilanmuodostusta, joka prosessoidaan hajautuneiden etäradioyksikköjen yli, ja joka hyväksikäyttää tilatason makrodiversiteettiä ja pessimististä ennustetta tiedonsiirtonopeudelle mahdollisten katveisten linkkien suhteen. Lisäksi otetaan huomioon käyttäjäkohtaiset latenssivaatimukset dynaamisessa liityntäverkossa, tavoitteena summatehon minimointi. Tämä stokastinen aikakeskiarvojen ongelma muunnetaan peräkkäisiksi aliongelmiksi, jotka ovat deterministisiä ja toisistaan riippumattomia, käyttäen Lyapunov-optimointia. Työssä kehitetään dynaaminen ohjausalgoritmi, joka mahdollistaa matalan kompleksisuuden suljetun muodon iteratiivisen keilanmuodostusratkaisun tehokkaan toteutuksen. Tämän lisäksi, hyväksikäyttämällä datajonojen ruuhkautumista ja kanavatietoa, toteutetaan CoMP-RRU:n palvelujoukon proaktiivinen ja dynaaminen valikointi. Latenssin ja akun keskinäisriippuvuuteen liittyvää jonodynamiikkaa tutkitaan yhtäaikainen informaation ja tehonlähetys (SWIPT) järjestelmissä, tavoitteena tehdä yhteiskeilanmuodostu

We consider a fully digital massive multiple-input multiple-output architecture with low-resolution analog-to-digital/digital-to-analog converters (ADCs/DACs) at the base station (BS) and analyze the performance trade-off between the number of BS antennas, the resolution of the ADCs/DACs, and the bandwidth. Assuming a hardware power consumption constraint, we determine the relationship between these design parameters by using a realistic model for the power consumption of the ADCs/DACs and the radio frequency chains. Considering uplink pilot-aided channel estimation, we build on the Bussgang decomposition to derive tractable expressions for uplink and downlink ergodic achievable sum rates. Numerical results show that the ergodic performance is boosted when many BS antennas with very low resolution (i.e., 2 to 3 bits) are adopted in both the uplink and the downlink.

Unmanned aerial vehicles (UAVs) are the emerging vital components of millimeter wave (mmWave) wireless systems. Accurate beam alignment is essential for efficient beam based mmWave communications of UAVs with base stations (BSs). Conventional beam sweeping approaches often have large overhead due to the high mobility and autonomous operation of UAVs. Learning-based approaches greatly reduce the overhead by leveraging UAV data, like position to identify optimal beam directions. In this paper, we propose a reinforcement learning (RL)-based framework for UAV-BS beam alignment using deep Q-Network (DQN) in a mmWave setting. We consider uplink communications where the UAV hovers around 5G new radio (NR) BS coverage area, with varying channel conditions. The proposed learning framework uses the location information to maximize data rate through the optimal beam-pairs efficiently, upon every communication request from UAV inside the multi-location environment. We compare our proposed framework against Multi-Armed Bandit (MAB) learning-based approach and the traditional exhaustive approach, respectively and also analyse the training performance of DQN-based beam alignment over different coverage area requirements and channel conditions. Our results show that the proposed DQN-based beam alignment converge faster and generic for different environmental conditions. The framework can also learn optimal beam alignment comparable to the exhaustive approach in an online manner under real-time conditions.

This paper studies a power splitting (PS)-based simultaneous wireless information and power transfer (SWIPT) multi-user system. Specifically, an optimization problem is formulated to minimize the average transmit power of the base station (BS) by jointly optimizing the transmit beamformer and receive PS ratios, while meeting user-specific latency and energy harvesting (EH) requirements. We employ the Lyapunov optimization framework and provide a dynamic control algorithm for the time-average problem. The coupled and non-convex constraints are handled via the Successive Convex Approximation (SCA) technique, and a low-complexity iterative algorithm, where each step is computed in closed-form, is proposed by solving a system of Karush-Kuhn-Tucker (KKT) optimality conditions. The numerical results provide insights on the robustness of the proposed design to realize a power-efficient SWIPT system while ensuring latency and EH requirements in a dynamic network.

This paper presents an analytical framework for the data detection in massive multiple-input multiple-output uplink systems with 1-bit analog-to-digital converters (ADCs). Considering the single-user case, we provide closed-form expressions of the expected value and the variance of the estimated symbols when maximum ratio combining is adopted at the base station (BS) along with their asymptotic behavior at high signal-to-noise ratio (SNR). These results are exploited to enhance the performance of maximum likelihood detection by taking into account the dispersion of the estimated symbols about their expected values. The symbol error rate with 1-bit ADCs is evaluated with respect to the number of BS antennas, the SNR, and the pilot length used for the channel estimation. The proposed analysis highlights a fundamental SNR trade-off, according to which operating at the right SNR considerably improves the data detection accuracy.

This paper considers a multi-user multiple-input-single-output (MU-MISO) broadcast scenario with power splitting (PS) based simultaneous wireless information and power transfer (SWIPT). Specifically, we propose a novel joint transmit beamforming and receive PS strategy aiming to minimize the total transmit power of the base station (BS) under user-specific latency constraints. We use the Lyapunov optimization framework and derive a dynamic control algorithm to transform the long-term time-average sum-power minimization problem into a sequence of deterministic and independent subproblems. Furthermore, the combinations of coupled and non-convex constraints are handled using semidefinite relaxation (SDR) and fractional programming (FP) techniques. The numerical examples illustrate the trade-offs between average transmit power and harvested power while ensuring the user-specific latency requirements.

This paper considers a massive single-input single-output (SIMO) system with 1-bit analog-to-digital converters (ADCs) at the base station (BS). In this context, the stochastic resonance phenomenon can be exploited together with the massive number of antennas to enable the symbol detection. In this regard, we investigate the statistics of the estimated symbols when maximum ratio combining (MRC) is adopted at the BS. In addition, assuming 16 quadrature amplitude modulation (16-QAM), we propose three different symbol detectors that are designed based on the mean and variance of the MRC outputs. Through numerical evaluations, we show that the proposed symbol detectors can significantly enhance the symbol error rate performance of massive SIMO systems with 1-bit ADCs.

We present an analytical framework for the channel estimation and the data detection in massive multiple-input multiple-output uplink systems with 1-bit analog-to-digital converters (ADCs) and i.i.d. Rayleigh fading. First, we provide closed-form expressions of the mean squared error (MSE) of the channel estimation considering the state-of-the-art linear minimum MSE estimator and the class of scaled least-squares estimators. For the data detection, we provide closed-form expressions of the expected value and the variance of the estimated symbols when maximum ratio combining is adopted, which can be exploited to efficiently implement minimum distance detection and, potentially, to design the set of transmit symbols. Our analytical findings explicitly depend on key system parameters such as the signal-to-noise ratio (SNR), the number of user equipments, and the pilot length, thus enabling a precise characterization of the performance of the channel estimation and the data detection with 1-bit ADCs. The proposed analysis highlights a fundamental SNR trade-off, according to which operating at the right noise level significantly enhances the system performance.

The fundamental challenge of the millimeter-wave (mmWave) frequency band is the sensitivity of the radio channel to blockages, which gives rise to unstable connectivity and impacts the reliability of a system. To this end, multi-point connectivity is a promising approach for ensuring the desired rate and reliability requirements. A robust beamformer design is proposed to improve the communication reliability by exploiting the spatial macro-diversity and a pessimistic estimate of rates over potential link blockage combinations. Specifically, we provide a blockage-aware algorithm for the weighted sum-rate maximization (WSRM) problem with parallel beamformer processing across distributed remote radio units (RRUs). Combinations of non-convex and coupled constraints are handled via successive convex approximation (SCA) framework, which admits a closed-form solution for each SCA step, by solving a system of Karush-Kuhn-Tucker (KKT) optimality conditions. Unlike the conventional coordinated multi-point (CoMP) schemes, the proposed blockage-aware beamformer design has, per-iteration, computational complexity in the order of RRU antennas instead of system-wide joint transmit antennas. This leads to a practical and computationally efficient implementation that is scalable to any arbitrary multi-point configuration. In the presence of random blockages, the proposed schemes are shown to significantly outperform baseline scenarios and result in reliable mmWave communication.

Cell-Free massive MIMO system performance can be improved with cooperative beamforming strategies. Typically, local beamforming strategies are assumed at base stations (BSs) to avoid extensive channel state information exchange via backhaul links. A fully distributed framework relying on a novel over-the-air (OTA) signaling mechanism has been recently proposed to design cooperative beamformers for cell-free massive MIMO systems. However, the existing distributed exact solution has a suboptimal global convergence behaviour because of outdated information used at each BS. This paper proposes a Newton method with an adaptive regularization to design uplink receivers for the fully distributed framework. Numerical results show a significantly faster convergence of the proposed method compared to the distributed exact solution and gradient methods.

Random matrix (RM) methods are exploited to develop radio resource management techniques for future wireless networks wherein the dimensions of the system, including bandwidth, the number of nodes and antennas, are expected to be large. RM methods provide deterministic approximations of network performance, which become arbitrarily tight as the dimensions increase in size. The focus areas of this thesis include non-orthogonal multiple access (NOMA); coordinated multi-point (CoMP); and two-stage beamforming (TSB), which combines large-dimensional statistical pre/postprocessing with lower dimensional digital processing. A multi-carrier NOMA system based on low density spreading (LDS) in the frequency dimension is considered. The allocation of resources, solely based on statistical channel-state-information (CSI), is studied to maximize the ergodic sum-rate subject to constraints on the sparsity of the LDS-codes. While the optimum is attainable only via exhaustive search, a low complexity method with minimal coordination overhead is proposed that yield close-to-optimum resource allocation solutions. Minimum power beamforming with user-specific rate constraints is considered in a downlink CoMP network. Deterministic expressions in terms of statistical CSI are derived for user-specific inter-cell interference (ICI) strength. Relying on the deterministic ICI terms as coordination messages, low-complexity distributed coordination algorithms, suitable for dense networks with limited backhaul, are proposed. The algorithms decouple the sub-problems at base stations (BSs) as long as the CSI statistics remain unchanged. The maximization of minimum signal-to-interference-plus-noise ratio (SINR) is considered in a dense CoMP network with random topology. Deterministic approximations are derived for the optimal SINR assignment in terms of system parameters such as cell radius, load, and power-budgets at BSs. The results enable the assessment of network performance based o, Tiivistelmä Satunnaismatriiseja (RM) käyttäviä menetelmiä hyödynnetään kehitettäessä radioresurssien hallintatekniikoita tuleville langattomille verkoille, joissa järjestelmän mittojen, kuten kaistanleveyden sekä verkkoelementtien ja antennien määrän, odotetaan olevan suuria. RM-menetelmät tarjoavat deterministisiä arvioita verkon suorituskyvystä, jotka tarkentuvat järjestelmän ulottuvuuksien kasvaessa suuriksi. Erityistä huomiota kiinnitetään seuraaviin: ei-ortogonaaliseen monipääsytekniikkaan (NOMA); koordinoituihin monipistelähetyksiin (CoMP); ja kaksivaiheiseen keilanmuodostukseen (TSB), jossa yhdistyvät suuriulotteinen tilastollinen esi-/jälkikäsittely ja alemman ulottuvuuden digitaalinen käsittely. Aluksi tarkastellaan monen kantoaallon NOMA-järjestelmä, joka perustuu matalatiheyksiseen hajautukseen taajuustasossa (LDS). Resurssien allokointia, joka perustuu yksinomaan tilastolliseen kanavatilatietoon (CSI), tutkitaan ergodisen kokonaistiedonsiirtonopeuden maksimoimiseksi ottaen huomioon LDS-koodien tiheysrajoitukset. Koska optimaalinen ratkaisu on saavutettavissa vain täydellisen haun avulla, tässä työssä ehdotetaan matalan monimutkaisuuden menetelmää, joka vaatii minimaalista koordinointia ja joka tuottaa lähes-optimaalisia resurssien allokointiratkaisuja. Seuraavaksi kehitetään lähetystehon minimoivia keilanmuodostusmenetelmiä laskevan siirtotien CoMP-verkossa ottaen huomioon käyttäjäkohtaiset vähimmäistiedonsiirtovaatimukset. Työssä johdetaan tilastolliseen kanavatietoon pohjautuvia deterministisiä matemaattisia lausekkeita käyttäjäkohtaisille solujen välistä häiriötä kuvaaville (ICI) termeille. Determinististen ICI-termien käyttö koordinointisanomina mahdollistaa matalan monimutkaisuuden hajautetun toteutuksen tiheissä verkoissa, joiden runkoliityntäyhteys on rajallinen. Signaali-häiriö-plus-kohinasuhteen (SINR) maksimointia tarkastellaan tiheässä satunnaistopologiaan perustuvassa CoMP-verkossa. Deterministisiä lausekkeita johdetaan optimaalisille SINR-ar

Most works on cell-free massive multiple-input multiple-output (MIMO) consider non-cooperative precoding strategies at the base stations (BSs) to avoid extensive channel state information (CSI) exchange via backhaul signaling. However, considerable performance gains can be accomplished by allowing coordination among the BSs. This paper proposes the first distributed framework for cooperative precoding design in cell-free massive MIMO (and, more generally, in joint transmission coordinated multi-point) systems that entirely eliminates the need for backhaul signaling for CSI exchange. A novel over-the-air (OTA) signaling mechanism is introduced such that each BS can obtain the same cross-term information that is traditionally exchanged among the BSs via backhaul signaling. The proposed distributed precoding design enjoys desirable flexibility and scalability properties, as the amount of OTA signaling does not scale with the number of BSs or user equipments. Numerical results show fast convergence and remarkable performance gains as compared with non-cooperative precoding design. The proposed scheme can also outperform the centralized precoding design under realistic CSI acquisition.

A multi-antenna broadcast channel scenario is considered where a base station delivers contents to cache-enabled user terminals. A joint design of coded caching (CC) and multigroup multicast beamforming is proposed to benefit from spatial multiplexing gain, improved interference management and the global CC gain, simultaneously. The developed general content delivery strategies utilize the multiantenna multicasting opportunities provided by the CC technique while optimally balancing the detrimental impact of both noise and inter-stream interference from coded messages transmitted in parallel. Flexible resource allocation schemes for CC are introduced where the multicast beamformer design and the receiver complexity are controlled by varying the size of the subset of users served during a given time interval, and the overlap among the multicast messages transmitted in parallel, indicated by parameters α and β, respectively. Degrees of freedom (DoF) analysis is provided showing that the DoF only depends on α while it is independent of β. The proposed schemes are shown to provide the same degrees-of-freedom at high signal-to-noise ratio (SNR) as the state-of-art methods and, in general, to perform significantly better, especially in the finite SNR regime, than several baseline schemes.

The sensitivity of millimeter-wave (mmWave) radio channel to blockage is a fundamental challenge in achieving low-latency and reliable connectivity. In this paper, we explore the viability of using coordinated multi-point (CoMP) transmission for a delay bounded and reliable mmWave communication. We provide an iterative algorithm for the time-average sum-power-minimization problem by solving a system of Karush-Kuhn-Tucker (KKT) optimality conditions. We use the Lyapunov optimization framework and derive a dynamic control algorithm to transform a time-average stochastic problem into a sequence of deterministic subproblems. Furthermore, for the robust beamformer design, we consider a pessimistic estimate of the user-specific rate, assuming that a portion of CoMP links would be blocked during the data transmission phase, while ensuring the average latency requirements. The numerical examples illustrate that in the presence of random blockages, the proposed method outperforms baseline scenarios and results in energy-efficient, high-reliability and low-latency mmWave communication.

Integrated access and backhaul (IAB) network consists of base station (BS), relay nodes (RNs), and user-equipments (UEs), where BS and RNs exchange UE data via wireless in-band backhaul while sharing the same frequency-time resources with access links. In this paper, a flexible time-division-duplex (TDD)-based IAB network is considered where RNs and BS are assigned to distinct uplink (UL) or downlink (DL) transmission modes to mitigate conventional half-duplex (HD) loss at RNs. An iterative beamformer design is proposed to manage the resulting cross-channel interference and to allocate wireless backhaul and access resources jointly over two consecutive data delivery intervals required for communications between the BS and UEs through HD RNs. Dynamic traffic behavior is handled via weighted queue minimization objective, and user-specific UL/DL queues are also introduced at RNs to guarantee reliable end-to-end data delivery. Bi-directional forward-backward training via spatially precoded over-the-air pilot signaling is employed to allow decentralized beamformer design across all the nodes. A novel user allocation method is proposed to assign UEs to BS or RNs based only on long-term channel statistics and some practical IAB limitations. The numerical examples illustrate the superior system performance of the considered flexible IAB in comparison to the conventional HD relaying system.

Integrated access and backhaul (IAB) networks consist of IAB-donor, IAB-nodes, and user-equipments (UEs). Both IAB-donor and IAB-node provide access to UEs while IAB-donor and IAB-nodes exchange UEs data via wireless in-band backhaul using the same frequency-time resources shared with access links. Multi-antenna beamformer techniques can be used to mitigate the complicated cross-link interference scenarios arising from IAB systems. In this paper, an iterative beamformer design with the weighted queue minimization (WQM) objective is proposed for the time-division-duplexed (TDD) based IAB system. In the considered TDD based IAB model, in a given timeslot, IAB-nodes and IAB-donor are assumed to be different uplink (UL)/downlink (DL) transmission modes to mitigate conventional half-duplex loss. Also, the beamformer design is carried out over two timeslots, considering both UL and DL transmission at each node. Specifically, user-specific UL/DL queues are introduced at the IAB-nodes to guarantee the BS to/from UE data delivery. The proposed beamformer solution is based on the iterative evaluation of Karush-Kuhn-Tucker (KKT) conditions of the optimization problem, which can practically be implemented in both centralized and decentralized manner. The numerical examples illustrate the superior system performance of the proposed method in comparison to the conventional half-duplex relaying system.

Low density spreading non-orthogonal multiple- access (LDS-NOMA) is considered where K single-antenna user equipments (UEs) communicate with a base station (BS) over F fading sub-carriers. Each UE k spreads its data symbol over d k <; F sub-carriers. Given d k, ∀k as design parameters, we characterize the resource allocation solutions that closely maximize the ergodic mutual information (EMI) in a scenario where the BS assigns resources solely based on the UEs’ pathlosses. Conducting analysis in asymptotic limit where F, K, and d k, ∀k converge to +∞ at the same rate, we present EMI in terms of a deterministic equivalent plus a residual term. The deterministic equivalent is given in terms of pathloss values and LDS-codes, and the small residual term scales as O(1/d 2 ) where d = min{d k, ∀k}. We formulate an optimization problem to get the set C̅* of all spreading codes, irrespective of sparsity constraints, which maximize the deterministic equivalent of EMI. The spreading codes in C̅* with desired sparsity are obtained via a simple and efficient algorithmic solution. In the finite regime, the residual term is shown to be a small incremental gain for the sparse solutions in C̅*, which is dictated mainly by d k, ∀k values. Accordingly, we show that the solutions in C̅* with desired sparsity yield close to optimum values of EMI in the finite regime. Numerical simulation validates the attainable spectral efficiency enhancement as compared to regular, and random spreading.