Unmanned aerial vehicles (UAVs) attract increasing attention for various wireless network applications by using UAVs' reliable line-of-sight (LoS) paths in air-ground connections and their flexible placement and movement. As such, the wireless network architecture is becoming three-dimensional (3D), incorporating terrestrial and aerial network nodes, which is more dynamic than the traditional terrestrial communications network. Despite the UAVs' advantages of high LoS path probability and flexible mobility, the challenges of UAV communications need to be considered in the design of integrated air-ground networks, such as spectrum sharing, air-ground interference management, energy-efficient and cost-effective UAV-assisted communications. On the other hand, in wireless networks, users request not only reliable communication services but also execute computation-intensive and latency-sensitive tasks. As one of the enabling technologies in wireless networks, edge computing is proposed to offload users' computation tasks to edge servers to reduce users' latency and energy consumption. However, this requires efficient utilization of both communication resources and computation resources. Furthermore, integrating UAVs into edge computing networks brings many benefits, such as enhancing offloading ability and extending offloading coverage region. This dissertation makes a series of fundamental contributions to UAVs and edge computing in wireless networks that include: 1) Reliable UAV communications, 2) Efficient edge computing schemes, and 3) Integration of UAV and edge computing. In the first contribution, we investigate UAV spectrum access and UAV swarm-enabled aerial reconfigurable intelligent surface (SARIS) for achieving reliable UAV communications. On the one hand, we study a 3D spectrum sharing between device-to-device (D2D) and UAVs communications. Specifically, UAVs perform spatial spectrum sensing to opportunistically access the licensed channels occupied by the D2D communications of ground users. The results show that UAVs' optimal spatial spectrum sensing radius can be obtained given specific network parameters. On the other hand, we study the beamforming and placement design for SARIS networks in downlink transmissions. We consider that the direct links between the ground base station (BS) and mobile users are blocked due to obstacles in the urban environment. SARIS assists the BS in reflecting the signals to randomly distributed mobile users. The results show that the proposed SARIS network significantly improves the weighted sum-rate for ground users, and the placement design plays an essential role in the overall system performance. In the second contribution, we develop a joint communication and computation resource allocation scheme for vehicular edge computing (VEC) systems. The full channel state information (CSI) in VEC systems is not always available at roadside units (RSUs). The channel varies fast due to vehicles' mobility, and it is pretty challenging to estimate CSI and feed back the RSUs for processing the VEC algorithms. To address the above problem, we introduce a large-scale CSI-based partial computation offloading scheme for VEC systems. Using deep learning and optimization tools, we minimize the users' energy consumption while guaranteeing their offloading latency and outage constraints. The results demonstrate that the introduced resource allocation scheme can significantly reduce the total energy consumption of users compared with other computation offloading schemes. In the third contribution, we present novel frameworks for integrating UAVs to edge computing networks to achieve improved computing performance. We study mobile edge computing (MEC) in air-ground integrated wireless networks, including ground computational access points (GCAPs), UAVs, and user equipment (UE), where UAVs and GCAPs cooperatively provide computation resources for UEs. The resource allocation algorithm is developed based on the block coordinate descent method by optimizing the subproblems of users' association, power control, bandwidth allocation, computation capacity allocation, and UAV placement. The results show the advantages of the introduced iterative algorithm regarding the reduced total energy consumption of UEs. Finally, we highlight directions for future works to advance the research presented in this dissertation and discuss its broader impact across the wireless networks industry and standard-making. / Doctor of Philosophy / The fifth-generation (5G) cellular network aims to achieve a high data rate by having greater bandwidth, deploying denser networks, and multiplying the antenna links' capacity. However, the current wireless cellular networks are fixed on the ground and thus pose many disadvantages. Moreover, the improved system performance comes at the cost of increased capital expenditures and operating expenses in wireless networks due to the enormous energy consumption at base stations (BS) and user equipment (UE). More spectrum and energy-efficient yet cost-effective technologies need to be developed in next-generation wireless networks, i.e., beyond-5G or sixth-generation (6G) networks. Recently, unmanned aerial vehicle (UAV) has attracted significant attention in wireless communications. Due to UAVs' agility and mobility, UAVs can be quickly deployed to support reliable communications, resorting to its line-of-sight-dominated connections in the air-ground channels. However, the sufficient available spectrum for extensive UAV communications is scarce, and the co-channel interference in air-air and air-ground connections need to be considered in the design of UAV networks. In addition to users' communication requests, users also need to execute intensive computation tasks with specific latency requirements. As such, edge computing has been proposed to integrate wireless communications and computing by offloading users' computation tasks to edge servers in proximity, reducing users' computation energy consumption and latency. Besides, integrating UAVs into edge computing networks makes efficient offloading schemes by leveraging the advantages of UAV communications. This dissertation makes several contributions that enhance UAV communications and edge computing systems performance, respectively, and present novel frameworks for UAV-assisted three-dimensional (3D) edge computing systems. This dissertation addresses the fundamental challenges in UAV communications, including spectrum sharing, interference management, UAV 3D placement, and beamforming, allowing broadband, wide-scale, cost-effective, and reliable wireless connectivity. Furthermore, this dissertation focuses on the energy-efficient vehicular edge computing systems and mobile edge computing systems, where the UAVs are applied to achieve 3D edge computing systems. To this end, various mathematical frameworks and efficient joint communication and computation resource allocation algorithms are proposed to design, analyze, optimize, and deploy UAV and edge computing systems. The results show that the proposed air-ground integrated networks can deliver spectrum-and-energy-efficient yet cost-effective wireless services, thus providing ubiquitous wireless connectivity and green computation offloading in the future beyond-5G or 6G wireless networks.

Millimeter-wave (mmWave) is widely investigated for indoor communication scenarios thanks to the available rich spectrum. However, the shortened antenna size and the high frequency make mmWave extra sensitive to blockages. Indoor dense space (IDS) is a specific type of indoor environment, where the compact geometry together with a high number of blocking objects and users make it hard to fulfill the data rate required by all of the users in the mmWave network. With the capability of redirecting signals, the reconfigurable intelligent surface (RIS) has the potential to overcome the attenuation brought by the blockage. Aside from the promising improvement in data rate brought by the RIS, the power supply for RIS is also a major concern in IDS due to the cabling and the batteries. Dynamic RIS has the capability of reconfiguring its phase-shifts to offer a higher gain in data rate with the price of consuming power. In comparison, by sacrificing the reconfigurability, static RIS does not require any power, cabling, or batteries but is expected to provide lower data rates. To find the balance between the performance and cost trade-off, the concept of self-sustainable RIS in IDS is proposed. This approach involves the utilization of specific RIS elements to harvest energy, thereby providing support for the power requirements of the RIS operation, consequently reducing the reliance on traditional cabling infrastructure. In this work, we compare the coverage extension effect brought by deploying static, dynamic, and self-sustainable RISs in the aircraft cabin which is a typical example of an IDS. To capture the propagation characteristics of a RIS in IDS, we first provide guidelines for modeling the RIS in the ray tracing (RT) simulator, and then we select the best locations to deploy RISs among three candidates. For each type of RIS deployment, we propose an optimization algorithm, which jointly configures the RIS phase-shifts and the time resources to provide the maximum equal achievable data rate for all of the users. Additionally, for the self-sustainable RIS, the working mode of each RIS element is also jointly configured such that each element is used either to reflect the incoming signal or to use the signal for energy harvesting. Based on the results, the signal propagation of a single base station (BS) can be extended from 3 rows to 11 rows by deploying static or dynamic RISs. The minimal achievable data rate is 35.4 Mbps with the static RISs and 45.3 Mbps with the dynamic RISs. The results indicate that due to the limitation of self-sustainable constraints, RISs with 16 elements are hard to cover the whole 11 rows in the considered cabin. Nevertheless, with self-sustainable RIS, 10 more UEs are covered compared to the case where no RIS is deployed. The minimal data rate with the help of the self-sustainable RISs within the coverage is 0.75 Mbps. The feasibility study shows that this energy requirement has a greater likelihood of being fulfilled as the number of elements in RIS increases. / Millimetervåg (mmWave) är allmänt undersökt för inomhuskommunikationsscenarier tack vare det tillgängliga rika spektrumet. Den förkortade antennstorleken och den höga frekvensen gör dock mmWave extra känslig för blockeringar. Indoor dense space (IDS) är en specifik typ av inomhusmiljö, där den kompakta geometrin tillsammans med ett stort antal blockerande objekt och användare gör det svårt att uppfylla den datahastighet som krävs av alla användare i mmWave-nätverket. Med förmågan att omdirigera signaler har reconfigurable intelligent surface (RIS) potentialen att övervinna dämpningen av blockeringen. Bortsett från den lovande förbättringen av datahastigheten som RIS ger, är strömförsörjningen för RIS också ett stort problem inom IDS på grund av kablarna och batterierna. Dynamic RIS har förmågan att omkonfigurera sina fasförskjutningar för att erbjuda en högre förstärkning i datahastighet med priset för att förbruka energi. I jämförelse, genom att offra omkonfigurerbarheten, kräver statisk RIS ingen ström, kablar eller batterier utan förväntas ge lägre datahastigheter. För att hitta balansen mellan prestanda och kostnadsavvägning föreslås konceptet med självförsörjande RIS i IDS. Detta tillvägagångssätt involverar användningen av specifika RIS-element för att skörda energi, vilket ger stöd för strömkraven för RIS-driften, vilket minskar beroendet av traditionell kabelinfrastruktur. I det här arbetet jämför vi den täckningsförlängningseffekt som uppstår genom att installera statiska, dynamiska och självförsörjande RIS i flygplanskabinen, vilket är ett typiskt exempel på en IDS. För att fånga utbredningsegenskaperna för en RIS i IDS ger vi först riktlinjer för modellering av RIS i ray tracing (RT)-simulatorn, och sedan väljer vi de bästa platserna för att distribuera RIS bland tre kandidater. För varje typ av RIS-distribution föreslår vi en optimeringsalgoritm, som gemensamt konfigurerar RIS-fasförskjutningarna och tidsresurserna för att tillhandahålla den maximalt lika möjliga datahastigheten för alla användare. Dessutom, för den självförsörjande RIS, är arbetsläget för varje RIS-element också gemensamt konfigurerat så att varje element används antingen för att reflektera den inkommande signalen eller för att använda signalen för energiskörd. Baserat på resultaten kan signalutbredningen av en enda base station (BS) utökas från 3 rader till 11 rader genom att distribuera statiska eller dynamiska RIS:er. Den minsta möjliga datahastigheten är 35,4 Mbps med statiska RIS och 45,3 Mbps med dynamiska RIS. Resultaten indikerar att på grund av begränsningen av självförsörjande begränsningar är RIS med 16 element svåra att täcka hela 11 rader i den övervägda kabinen. Ändå, med självförsörjande RIS, täcks 10 fler UE jämfört med fallet där ingen RIS är utplacerad. Den minimala datahastigheten med hjälp av de självförsörjande RIS:erna inom täckningen är 0,75 Mbps. Förstudien visar att detta energibehov har större sannolikhet att uppfyllas i takt med att antalet element i RIS ökar.

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

<p>    </p> <p>In order to advance next-generation communication systems, it is critical to enhance the state-of-the-art communication architectures, such as device-to-device (D2D), multiple- input multiple-output (MIMO), and intelligent reflecting surface (IRS), in terms of achieving high data rate, low latency, and high energy efficiency. In the first part of this dissertation, we address joint learning and optimization methodologies on cutting-edge network archi- tectures. First, we consider D2D networks equipped with MIMO systems. In particular, we address the problem of minimizing the network overhead in D2D networks, defined as the sum of time and energy required for processing tasks at devices, through the design for MIMO beamforming and communication/computation resource allocation. Second, we address IRS-assisted communication systems. Specifically, we study an adaptive IRS control scheme considering realistic IRS reflection behavior and channel environments, and propose a novel adaptive codebook-based limited feedback protocol and learning-based solutions for codebook updates. </p> <p><br></p> <p>Furthermore, in order for revolutionary innovations to emerge for future generations of communications, it is crucial to explore and address fundamental, long-standing open problems for communications, such as the design of practical codes for a variety of important channel models. In the later part of this dissertation, we study the design of practical codes for feedback-enabled communication channels, i.e., feedback codes. The existing feedback codes, which have been developed over the past six decades, have been demonstrated to be vulnerable to high forward/feedback noises, due to the non-triviality of the design of feedback codes. We propose a novel recurrent neural network (RNN) autoencoder-based architecture to mitigate the susceptibility to high channel noises by incorporating domain knowledge into the design of the deep learning architecture. Using this architecture, we suggest a new class of non-linear feedback codes that increase robustness to forward/feedback noise in additive White Gaussian noise (AWGN) channels with feedback. </p>