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While the notion of age of information (AoI) has recently emerged as an important concept for analyzing ultra-reliable low-latency communications (URLLC), the majority of the existing works have focused on the average AoI measure. However, an average AoI based design falls short in properly characterizing the performance of URLLC systems as it cannot account for extreme events that occur with very low probabilities. In contrast, in this paper, the main objective is to go beyond the traditional notion of average AoI by characterizing and optimizing a URLLC system while capturing the AoI tail distribution. In particular, the problem of vehicles’ power minimization while ensuring stringent latency and reliability constraints in terms of probabilistic AoI is studied. To this end, a novel and efficient mapping between both AoI and queue length distributions is proposed. Subsequently, extreme value theory (EVT) and Lyapunov optimization techniques are adopted to formulate and solve the problem. Simulation results shows a nearly two-fold improvement in terms of shortening the tail of the AoI distribution compared to a baseline whose design is based on the maximum queue length among vehicles, when the number of vehicular user equipment (VUE) pairs is 80. The results also show that this performance gain increases significantly as the number of VUE pairs increases.

In this paper, a novel concept of three-dimensional (3D) cellular networks, that integrate drone base stations (drone-BS) and cellular-connected drone users (drone-UEs), is introduced. For this new 3D cellular architecture, a novel framework for network planning for drone-BSs and latency-minimal cell association for drone-UEs is proposed. For network planning, a tractable method for drone-BSs’ deployment based on the notion of truncated octahedron shapes is proposed, which ensures full coverage for a given space with a minimum number of drone-BSs. In addition, to characterize frequency planning in such 3D wireless networks, an analytical expression for the feasible integer frequency reuse factors is derived. Subsequently, an optimal 3D cell association scheme is developed for which the drone-UEs’ latency, considering transmission, computation, and backhaul delays, is minimized. To this end, first, the spatial distribution of the drone-UEs is estimated using a kernel density estimation method, and the parameters of the estimator are obtained using a cross-validation method. Then, according to the spatial distribution of drone-UEs and the locations of drone-BSs, the latency-minimal 3D cell association for drone-UEs is derived by exploiting tools from an optimal transport theory. The simulation results show that the proposed approach reduces the latency of drone-UEs compared with the classical cell association approach that uses a signal-to-interference-plus-noise ratio (SINR) criterion. In particular, the proposed approach yields a reduction of up to 46% in the average latency compared with the SINR-based association. The results also show that the proposed latency-optimal cell association improves the spectral efficiency of a 3D wireless cellular network of drones.

Software-defined networking (SDN) provides an agile and programmable way to optimize radio access networks via a control-data plane separation. Nevertheless, reaping the benefits of wireless SDN hinges on making optimal use of the limited wireless fronthaul capacity. In this paper, the problem of fronthaul-aware resource allocation and user scheduling is studied. To this end, a two-timescale fronthaul-aware SDN control mechanism is proposed in which the controller maximizes the time-averaged network throughput by enforcing a coarse correlated equilibrium in the long timescale. Subsequently, leveraging the controller’s recommendations, each base station schedules its users using Lyapunov stochastic optimization in the short timescale, i.e., at each time slot. Simulation results show that significant network throughput enhancements and up to 40% latency reduction are achieved with the aid of the SDN controller. Moreover, the gains are more pronounced for denser network deployments.

Supporting ultra-reliable and low-latency communications (URLLC) is crucial for vehicular traffic safety and other mission-critical applications. In this paper, a novel proximity and quality-of-service-aware resource allocation framework for vehicle-to-vehicle (V2V) communication is proposed. The proposed scheme incorporates the physical proximity and traffic demands of vehicles to minimize the total transmission power over the allocated resource blocks (RBs) under reliability and queuing latency constraints. A Lyapunov framework is used to decompose the power minimization problem into two interrelated sub-problems: RB allocation and power optimization. To minimize the overhead introduced by frequent information exchange between the vehicles and the roadside unit (RSU), the resource allocation problem is solved in a semi-distributed fashion. First, a novel RSU-assisted virtual clustering mechanism is proposed to group vehicles into disjoint zones based on mutual interference. Second, a per-zone matching game is proposed to allocate RBs to each vehicle user equipment (VUE) based on vehicles’ traffic demands and their latency and reliability requirements. In the formulated one-to-many matching game, VUE pairs and RBs rank one another using preference relations that capture both the queue dynamics and interference. To solve this game, a semi-decentralized algorithm is proposed using which the VUEs and RBs can reach a stable matching. Finally, a latency-and reliability-aware power allocation solution is proposed for each VUE pair over the assigned subset of RBs. Simulation results for a Manhattan model show that the proposed scheme outperforms the state-of-art baseline and reaches up to 45% reduction in the queuing latency and 94% improvement in reliability.

Considering a Manhattan mobility model in vehicle-to-vehicle networks, this work studies a power minimization problem subject to second-order statistical constraints on latency and reliability, captured by a network-wide maximal data queue length. We invoke results in extreme value theory to characterize the statistics of extreme events in terms of the maximal queue length. By leveraging Lyapunov stochastic optimization to deal with network dynamics, we propose two queue-aware power allocation solutions. In contrast with the baseline, our approaches achieve lower mean and variance of the maximal queue length.

One of the many problems faced by current cellular network technology is the underutilization of the dedicated licensed spectrum of network operators. An emerging paradigm to solve this issue is to allow multiple operators to share some parts of each other’s spectrum. Previous works on spectrum sharing have failed to integrate the theoretical insights provided by recent developments in stochastic geometrical approaches to cellular network analysis with the objectives of network resource allocation problems. In this paper, we study the non-orthogonal spectrum assignment with the goal of maximizing the social welfare of the network, defined as the expected weighted sum rate of the operators. We adopt the many-to-one stable matching game framework to tackle this problem. Moreover, using the stochastic geometrical approach, we show that its solution can be both stable as well as socially optimal. To obtain the maxima of social welfare, the computation of the game theoretical solution using the generic Markov Chain Monte Carlo method is proposed. We also investigate the role of power allocation schemes using Q-learning, and we numerically show that the effect of resource allocation scheme is much more significant than the effect of power allocation for the social welfare of the system.

Recently, millimeter-wave (mmWave) bands have been postulated as a means to accommodate the foreseen extreme bandwidth demands in vehicular communications, which result from the dissemination of sensory data to nearby vehicles for enhanced environmental awareness and improved safety level. However, the literature is particularly scarce in regards to principled resource allocation schemes that deal with the challenging radio conditions posed by the high mobility of vehicular scenarios. In this paper, we propose a novel framework that blends together matching theory and swarm intelligence to dynamically and efficiently pair vehicles and optimize both transmission and reception beamwidths. This is done by jointly considering channel state information and queue state information when establishing vehicle-to-vehicle (V2V) links. To validate the proposed framework, simulation results are presented and discussed, where the throughput performance as well as the latency/reliability tradeoffs of the proposed approach are assessed and compared with several baseline approaches recently proposed in the literature. The results obtained in this paper show performance gains of 25% in reliability and delay for ultra-dense vehicular scenarios with 50% more active V2V links than the baselines. These results shed light on the operational limits and practical feasibility of mmWave bands, as a viable radio access solution for future high-rate V2V communications.

In vehicular scenarios context awareness is a key enabler for road safety. However, the amount of contextual information that can be collected by a vehicle is stringently limited by the sensor technology itself (e.g., line-of-sight, coverage, weather robustness) and by the low bandwidths offered by current wireless vehicular technologies such as DSRC/802.11p. Motivated by the upsurge of research around millimeter-wave vehicle-to-anything (V2X) communications, in this work we propose a distributed vehicle-to-vehicle (V2V) association scheme that considers a quantitative measure of the potential value of the shared contextual information in the pairing process. First, we properly define the utility function of every vehicle balancing classical channel state and queuing state information (CSI/QSI) with context information i.e., sensing content resolution, timeliness and enhanced range of the sensing. Next we solve the problem via a distributed many-to-one matching game in a junction scenario with realistic vehicular mobility traces. It is shown that when receivers are able to leverage information from different sources, the average volume of collected extended sensed information under our proposed scheme is up to 71% more than that of distance and minimum delay-based matching baselines.

Recently vehicle-to-vehicle (V2V) communication emerged as a key enabling technology to ensure traffic safety and other mission-critical applications. In this paper, a novel proximity and quality-of-service (QoS)-aware resource allocation for V2V communication is proposed. The proposed approach exploits the spatial-temporal aspects of vehicles in terms of their physical proximity and traffic demands, to minimize the total transmission power while considering queuing latency and reliability. Due to the overhead caused by frequent information exchange between vehicles and the roadside unit (RSU), the centralized problem is decoupled into two interrelated subproblems. First, a novel RSU-assisted virtual clustering mechanism is proposed to group vehicles in zones based on their physical proximity. Given the vehicles’ traffic demands and their QoS requirements, resource blocks are assigned to each zone. Second, leveraging techniques from Lyapunov stochastic optimization, a power minimization solution is proposed for each V2V pair within each zone. Simulation results for a Manhattan model have shown that the proposed scheme outperforms the baseline in terms of average queuing latency reduction up to 97% and significant improvement in reliability.

Ultra-reliability and low-latency are two key components in 5G networks. In this letter, we investigate the problem of ultra-reliable and low-latency communication (URLLC) in millimeter wave (mmWave)-enabled massive multiple-input multiple-output (MIMO) networks. The problem is cast as a network utility maximization subject to probabilistic latency and reliability constraints. To solve this problem, we resort to the Lyapunov technique whereby a utility-delay control approach is proposed, which adapts to channel variations and queue dynamics. Numerical results demonstrate that our proposed approach ensures reliable communication with a guaranteed probability of 99.99%, and reduces latency by 28.41% and 77.11% as compared to baselines with and without probabilistic latency constraints, respectively.

Future cellular networks need to support the ever-increasing demand of bandwidth-intensive applications and interconnection of people, devices, and vehicles. Small cell network (SCN)-based communication together with proximity- and social-aware connectivity is conceived as a vital component of these networks to enhancing spectral efficiency, system capacity, and quality-of-experience (QoE). To cope with diverse application needs for the heterogeneous ecosystem, radio resource management (RRM) is one of the key research areas for the fifth-generation (5G) network. The key goals of this thesis are to develop novel, self-organizing, and low-complexity resource management algorithms for emerging device-to-device (D2D) and vehicle-to-vehicle (V2V) wireless systems while explicitly modeling and factoring network contextual information to satisfy the increasingly stringent requirements. Towards achieving this goal, this dissertation makes a number of key contributions. First, the thesis focuses on interference management techniques for D2D-enabled macro network and D2D-enabled SCNs in the downlink, while leveraging users’ social-ties, dynamic clustering, and user association mechanisms for network capacity maximization. A flexible social-aware user association technique is proposed to maximize network capacity. The second contribution focuses on ultra-reliable low-latency communication (URLLC) in vehicular networks in which interference management and resource allocation techniques are investigated, taking into account traffic and network dynamics. A joint power control and resource allocation mechanism is proposed to minimize the total transmission power while satisfying URLLC constraints. To overcome these challenges, novel algorithms are developed by combining several methodologies from graph theory, matching theory and Lyapunov optimization. Extensive simulations validate the performance of the proposed approaches, outperforming state-of-the-art solutions. Notably, the results yield significant performance gains in terms of capacity, delay reductions, and improved reliability as compared with conventional approaches. Tiivistelmä Tulevaisuuden solukkoverkkojen pitää pystyä tukemaan yhä suurempaa kaistanleveyttä vaativia sovelluksia sekä yhteyksiä ihmisten, laitteiden ja ajoneuvojen välillä. Piensoluverkkoihin (SCN) pohjautuvaa tietoliikennettä yhdistettynä paikka- ja sosiaalisen tietoisuuden huomioiviin verkkoratkaisuihin pidetään yhtenä elintärkeänä osana tulevaisuuden solukkoverkkoja, joilla pyritään tehostamaan spektrinkäytön tehokkuutta, järjestelmän kapasiteettia sekä kokemuksen laatua (QoE). Radioresurssien hallinta (RRM) on eräs keskeisistä viidennen sukupolven (5G) verkkoihin liittyvistä tutkimusalueista, joilla pyritään hallitsemaan heterogeenisen ekosysteemin vaihtelevia sovellustarpeita. Tämän väitöstyön keskeisinä tavoitteina on kehittää uudenlaisia itseorganisoituvia ja vähäisen kompleksisuuden resurssienhallinta-algoritmeja laitteesta-laitteeseen (D2D) ja ajoneuvosta-ajoneuvoon (V2V) toimiville uusille langattomille järjestelmille, sekä samalla mallintaa ja tuottaa verkon kontekstikohtaista tietoa vastaamaan koko ajan tiukentuviin vaatimuksiin. Tämä väitöskirja edistää näiden tavoitteiden saavuttamista usealla keskeisellä tuloksella. Aluksi väitöstyössä keskitytään häiriönhallinnan tekniikoihin D2D:tä tukevissa makroverkoissa ja laskevan siirtotien piensoluverkoissa. Käyttäjän sosiaalisia yhteyksiä, dynaamisia ryhmiä sekä osallistamismekanismeja hyödynnetään verkon kapasiteetin maksimointiin. Verkon kapasiteettia voidaan kasvattaa käyttämällä joustavaa sosiaaliseen tietoisuuteen perustuvaa osallistamista. Toinen merkittävä tulos keskittyy huippuluotettavaan lyhyen viiveen kommunikaatioon (URLLC) ajoneuvojen verkoissa, joissa tehtävää resurssien allokointia ja häiriönhallintaa tutkitaan liikenteen ja verkon dynamiikka huomioiden. Yhteistä tehonsäädön ja resurssien allokoinnin mekanismia ehdotetaan kokonaislähetystehon minimoimiseksi samalla, kun URLLC rajoitteita noudatetaan. Jotta esitettyihin haasteisiin voidaan vastata, väitöstyössä on kehitetty uudenlaisia algoritmeja yhdistämällä graafi- ja sovitusteorioiden sekä Lyapunovin optimoinnin menetelmiä. Laajat tietokonesimuloinnit vahvistavat ehdotettujen lähestymistapojen suorituskyvyn, joka on parempi kuin uusimmilla nykyisillä ratkaisuilla. Tulokset tuovat merkittäviä suorituskyvyn parannuksia erityisesti kapasiteetin lisäämisen, viiveiden vähentämisen ja parantuneen luotettavuuden suhteen verrattuna perinteisiin lähestymistapoihin.

The aim of this thesis is to devise novel co-primary spectrum sharing (CoPSS) methods for future fifth generation (5G) networks and beyond. The target is to improve data rates of small cell networks (SCNs) in which mobile network operators (MNOs) share their dedicated frequency spectrum (spectrum pooling) or a common spectrum (mutual renting). The performance of the proposed methods is assessed through extensive system-level simulations. MNOs typically acquire exclusive usage rights for certain frequency bands and have little incentive to share spectrums with other operators. However, due to higher cost and spectrum scarcity at lower frequencies it is expected that efficient use of the spectrum in 5G networks will rely more on spectrum sharing than exclusive licenses. This is especially true for new higher candidate frequencies (> 6 GHz) that do not have a pre-existing spectrum regulation framework. In the first part of the thesis, we tackle the challenge of providing higher data rates within limited spectral resources. Each SCN MNO has its own dedicated spectrum, and each MNO defines a percentage of how much its spectrum it is willing to share. The idea of the proposed CoPSS algorithms is that the spectrum is dynamically shared among MNOs based on their spectrum utilization, which is shared among MNOs in the network. This way interference can be avoided and spectrum utilization is maximized. Unused resources are shared equally between overloaded MNOs for a given time instant. Thus, only short-term fairness among overloaded SCNs can be guaranteed. In the second part, we consider a multi-operator small cell network where MNOs share a common pool of radio resources. The goal is to ensure the long term fairness of spectrum sharing without coordination among small cell base stations. We develop a decentralized control mechanism for base stations using the Gibbs sampling based learning tool, which allocates suitable amount of the spectrum for each base station while avoiding interference from SCNs and maximizing the total network throughput. In the studied scenarios, we show the importance of coordination among MNOs when the dedicated spectrum is shared. However, when MNOs share a common spectrum, a decentralized control mechanism can be used to allocate suitable amounts of spectrum for each base station. The proposed algorithms are shown to be effective for different network layouts, by achieving significant data rate enhancements with a low overhead. Tiivistelmä Tämä väitöskirja keskittyy kehittämään uusia menetelmiä, joilla jaetaan taajuuksia useiden operaattoreiden kesken tulevista viidennen sukupolven verkoista alkaen. Päätavoite on parantaa tiedonsiirtonopeuksia sellaisissa piensoluverkoissa, joissa matkapuhelinoperaattorit jakavat joko heidän omia taajuusalueitaan tai heillä yhteisomistuksessa olevia taajuuksia. Kehitettyjen menetelmien suorituskykyä arvioidaan mittavien järjestelmätason simulointien avulla. Matkapuhelinoperaattorit tyypillisesti omistavat yksin tietyt taajuusalueet, eivätkä ole valmiita jakamaan niitä. On kuitenkin oletettu, että tulevaisuudessa matkapuhelinoperaattorit joutuvat jakamaan taajuuksia, koska taajuusalueet ovat kalliita ja niukkoja erityisesti matalilla taajuusalueilla. Korkeammat taajuusalueet (> 6 GHz) puolestaan muodostavat otollisen alustan tehokkaalle spektrin jaetulle käytölle, koska niillä ei ole vielä olemassa olevaa taajuussääntelyä. Väitöskirjan ensimmäisessä osassa keskitytään kasvattamaan tiedonsiirtonopeuksia kun jokainen matkapuhelinoperaattori omistaa oman taajuuskaistansa ja matkapuhelinoperaattorit määrittävät kuinka suuren prosentuaalisen osuuden ovat valmiita jakamaan. Esitettyjen algoritmien päätavoite on jakaa taajuuksia dynaamisesti matkapuhelinoperaattoreiden kesken. Algoritmeissa hyödynnetään tietoa matkapuhelinoperaattoreiden taajuuden käyttöasteesta, jonka matkapuhelinoperaattoritkommunikoivat toisilleen. Näin häiriö voidaan välttää ja taajuuden käyttö maksimoidaan. Käyttämättömät taajuudet jaetaan tasaisesti matkapuhelinoperaattorien kesken tietyllä ajanhetkellä. Näin voidaan taata lyhytaikainen oikeudenmukainen taajuuksien käyttö, mutta ei pitkäaikaista oikeudenmukaista taajuuksien käyttöä. Väitöskirjan toisessa osassa matkapuhelinoperaattorit jakavat yhteisomistuksessa olevia taajuuksia. Tavoitteena on saavuttaa pitkäaikainen taajuuksien oikeudenmukainen käyttö, kun piensoluverkot eivät kommunikoi keskenään. Työssä kehitetään piensoluverkoille hajautettu algoritmi, joka perustuu oppimistyökaluun Gibbs-näytteistys. Näin saadaan allokoitua jokaiselle tukiasemalle tarvittava määrä taajuusresursseja niin, että häiriö tukiasemien välillä minimoidaan ja koko piensoluverkon suorituskyky maksimoidaan. Tutkituissa skenaarioissa osoitetaan matkapuhelinoperaattoreiden välisen koordinaation tärkeys, kun jaetaan omia taajuusalueita. Toisaalta kun operaattorit jakavat yhteisomistuksessa olevia taajuuksia on mahdollista käyttää algoritmeja, joissa ei ole koordinaatiota matkapuhelinoperaattoreiden kesken. Väitöskirjassa vahvistetaan kehitettyjen algoritmien olevan tehokkaita ja sopivan monenlaisiin verkkoympäristöihin saavuttaen merkittäviä parannuksia tiedonsiirtonopeuteen ilman suuria kustannuksia.

With the unprecedented growth in mobile data traffic and network densification, the emerging fifth-generation (5G) wireless network warrants a paradigm shift with respect to system design and technological enablers. In this regard, the prime motivation of this thesis is to propose an integrated access-backhaul (IAB) framework to dynamically schedule users, while efficiently providing a wireless backhaul to dense small cells and mitigating interference. In addition, joint resource allocation and interference mitigation solutions are proposed for two-hop and multi-hop self-backhauled millimeter wave (mmWave) networks. The first contribution of this thesis focuses on a multi-user two-hop relay cellular system in which a massive antenna array enabled macro base station (BS) simultaneously provides high beamforming gains to outdoor users, and wireless backhauling to outdoor small cells. Moreover, a hierarchical interference mitigation scheme is applied to efficiently mitigate cross-tier and co-tier interference. In the second contribution, a multi-hop self-backhauled mmWave communication scenario is studied whereby a joint multi-hop multi-path selection and rate allocation framework is proposed to enable Gbps data rates with reliable communications. Using reinforcement learning techniques, a dynamic and efficient re-routing solution is proposed to cope with blockage and latency constraints. Finally, a risk-sensitive learning solution is leveraged to provide high-reliability and low-latency communications. In summary, the dissertation analyses key trade-offs between (i) capacity and latency, (ii) reliability and network density. Extensive simulation results were carried out to verify the performance gains of the proposed algorithms compared to several baselines and for different network settings. Key findings show significant improvements in terms of higher data rates, lower latency, and reliable communications with some trade-offs. Tiivistelmä Liikkuvan dataliikenteen ennennäkemättömän kasvun ja verkkojen tihentymisen seurauksena pian käyttöön tulevien viidennen sukupolven (5G) langattomien verkkojen järjestelmäsuunnittelua ja teknologisten mahdollistajien käyttöä on täytynyt lähestyä kokonaan uudesta näkökulmasta. Niinpä tämän väitöstyön johtavana ajatuksena on ehdottaa integroitua verkkoon pääsyn ja runkoverkkoyhteyden muodostamismallia, jossa käyttäjät resursoidaan dynaamisesti ja samalla muodostetaan tehokkaat runkoverkkoyhteydet piensoluille. Tätä varten tutkitaan resurssiallokaation ja häiriöiden lieventämisen yhteisratkaisuja, jotka tukevat kahden tai useamman hypyn yhteyksiä ja samanaikaista runkoverkkoyhteyden luomista millimetriaaltoalueen verkoissa. Työn alkuosa keskittyy usean käyttäjän välitinavusteiseen kahden hypyn solukkoverkkoon, jossa makrotukiasemassa käytetään suurta antenniryhmää muodostamaan samanaikaisesti suuren vahvistuksen antennikeiloja käyttäjälinkeille ja langattomalle runkoyhteysosuudelle. Lisäksi sovelletaan hierarkkista häiriönvaimennusmenetelmää saman kerroksen ja kerrosten välisen häiriön tehokkaaseen vähentämiseen. Työn seuraavassa osassa arvioidaan usean hypyn runkoverkkoyhteyden muodostuksen tutkimusongelmaa millimetrialueen kommunikaatiossa kehittämällä yhdistetty menetelmä usean hypyn monipolkuvalinnalle ja tiedonsiirtoresurssien allokoinnille. Tällä tähdätään gigabittiluokan datanopeuksiin ja luotettavaan tietoliikenteeseen millimetrialueella. Vahvistavan oppimisen tekniikan avulla esitellään dynaaminen ja tehokas uudelleenreitityskonsepti toimimaan esto- ja viiverajoitusten kanssa. Lopuksi hyödynnetään riskisensitiivistä oppimista ja antennidiversiteettitekniikoita suuren luotettavuuden ja pienen latenssin saavuttamiseksi millimetrialueen tiedonsiirrossa. Näiden avulla analysoidaan kaupankäyntiä esimerkiksi (i) kapasiteetin ja latenssin sekä (ii) luotettavuuden ja verkon tiheyden/kuormituksen välillä. Mittavien suoritettujen simulointien avulla osoitetaan ehdotettujen algoritmien suorituskykyedut suhteessa tunnettuihin verrokkeihin useissa eri skenaarioissa. Tulosten perusteella saavutetaan merkittäviä kustannussäästöjä infrastruktuurin ja runkoverkon osalta sekä päästään suuriin datanopeuksiin ja parannuksiin pienen latenssin luotettavassa tietoliikenteessä.