Electronic band structure in the solid and its relation to the energy gap of the monomer is all about studying how intermolecular interactions change electronic structure. In experimental studies this results in broader absorption bands and by extension a lowering of the LUMO and raising of HOMO energy to the conduction and valence band edges respectively. This electronic change involves splitting of the molecular energy levels into bands of non-degenerate energies and can be calculated either quantum mechanically (QM) or by classical force field models through the change in ionization potential (IP) and electron affinity (EA), called the apparent polarization energy, and its relation to HOMO and LUMO through Koopman’s and Janak’s theorem. The study of the formation of a ‘band’ like structure is important in regimes and systems where conventional quantum mechanical (QM) methods become infeasible. Specifically, when systems are non-periodic and plane wave approximations fail, such as in amorphous structures, or in regimes between where the plane wave bulk approximation and the gas phase single molecule QM methods where the scaling of conventional gas phase atomic orbital methods becomes exorbitantly costly and the plane wave approximation fails for open systems. Therefore, the objective of this work is to highlight the changing optoelectronic properties of molecular solids within this regime using both density functional theory and molecular mechanics. The scalability of DFT limits it to multimer systems, leaving the larger nanoscale materials to be studied using molecular mechanics. Here we have utilized a variety of dispersion sensitive functionals in order to characterize the intermolecular interactions and splitting energies in small multimers of some of the smallest oligoacene species, benzene and anthracene. Benzene and anthracene nanoclusters from 0.8 to 5.0 nm in radius have had their changes in electronic band energy calculated due to polarization using the AMOEBA force field and bulk values have also been extrapolated. AMOEBA’s explicit polarization terms allow for direct handling of the polarization energy, control of nanocluster size and shape in a regime that QM methods cannot probe efficiently, and the ability to specify the position of charge carriers in order to examine specific electronic surface behavior. Using differing DFT methods the change in the HOMO and LUMO energy from the single molecule state to multimers of the size of 10 and 12 units for anthracene and benzene respectively. The HOMO band of benzene was raised by ~0.3 eV and LUMO lowered by 0.35 eV. In anthracene the HOMO was lowered by ~0.1 eV and the LUMO by ~0.15 eV. These values remain within 0.1 eV across all dispersion functionals. Using Ren’s parameterization procedure and MP2 for the AMOEBA force field he apparent polarization was calculated. The extrapolated values for the change in the HOMO and LUMO of benzene from single molecule to bulk were 1.42 eV and 0.49 eV respectively. For anthracene the crystalline bulk changes the HOMO and LUMO by 1.34 eV and 1.16 eV respectively. The regression for bulk extrapolation also predicts that benzene clusters of 12 units will be 0.77 eV for HOMO and -0.41 eV for LUMO. Similarly for an anthracene cluster made up of 10 molecular units the apparent polarization is predicted through linear regression to be 0.58 eV for HOMO and 0.53 eV for LUMO.

Fifth-Generation (5G) and sixth-Generation (6G) are new global wireless standards providing everyone and everything, machines, objects, and devices, with massive network capacity. The technological advances in wireless communication enable 5G and 6G networks to support resource and computation-hungry services such as smart agriculture and smart city applications. Among these advances are two state-of-the-art technologies: Carrier Aggregation (CA) and Multi Access Edge Computing (MEC). CA unlocks new sources of spectrum in both the mid-band and high-band radio frequencies. It provides the unique capability of aggregating several frequency bands for higher peak rates, and increases cell coverage. The latter is obtained by activating the Component Carriers (CC) in low-band and mid-band frequency (below 7 GHz) while 5G high-band (above 24GHz) delivers unprecedented peak rates with poorer Uplink (UL) coverage. MEC provides computing and storage resources with sufficient connectivity close to end users. These execution resources are typically within/at the boundary of access networks providing support for application use cases such as Augmented Reality (AR)/Virtual Reality (VR). The key technology in MEC is task offloading, which enables a user to offload a resource-hungry application to the MEC hosts to reduce the cost (in terms of energy and latency) of processing the application. This thesis focuses on using CA and task offloading in 5G and 6G wireless networks. These advanced infrastructures are an enabler for many broader use cases, e.g., autonomous driving and Internet of Things (IoT) applications. However, the pertinent problems are the high dimensional ones with combinatorial characteristics. Furthermore, the time-varying features of the 5G/6G wireless networks, such as the stochastic nature of the wireless channel, should be concurrently met. The above challenges can be tackled by using data-driven techniques and Machine Learning (ML) algorithms to derive intelligent and autonomous resource management techniques in the 5G/6G wireless networks. The resource management problems in these networks are sequential decision-making problems, additionally with conflicting objectives. Therefore, among the ML algorithms, the ones based on the Reinforcement Learning (RL), constitute a promising tool to make a trade-off between the conflicting objectives of the resource management problems in the 5G/6G wireless networks, are used. This research considers the objective of maximizing the achievable rate and minimizing the users’ transmit power levels in the MEC-enabled network. Additionally, we try to simultaneously maximize the network capacity and improve the network coverage by activating/deactivating the CCs. Compared with the derived schemes in the literature, our contributions are two folded: deriving distributed resource management schemes in 5G/6G wireless networks to efficiently manage the limited spectrum resources and meet the diverse requirements of some resource-hungry applications, and developing intelligent and energy-aware algorithms to improve the performance in terms of energy consumption, delay, and achievable rate.

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