The demand of mobile users with high data-rate services continues to increase. To satisfy the needs of such mobile users, operators must continue to enhance their existing networks. The radio interface is a well-known bottleneck because the radio spectrum is limited and therefore expensive. Efficient use of the radio spectrum is, therefore, very important. To utilise the spectrum efficiently, any of the channels can be used simultaneously in any of the cells as long as interference generated by the base stations using the same channels is below an acceptable level. In cellular networks based on Orthogonal Frequency Division Multiple Access (OFDMA), inter-cell interference reduces the performance of the link throughput to users close to the cell edge. To improve the performance of cell-edge users, a technique called Coordinated Multi-Point (CoMP) transmission is being researched for use in the next generation of cellular networks. For a network to benefit from CoMP, its utilisation of resources should be scheduled efficiently. The thesis focuses on the resource scheduling algorithm development for CoMP joint transmission scheme in OFDMA-based cellular networks. In addition to the algorithm, the thesis provides an analytical framework for the performance evaluation of the CoMP technique. From the system level simulation results, it has been shown that the proposed resource scheduling based on a joint maximum throughput provides higher spectral efficiency compared with a joint proportional fairness scheduling algorithm under different traffic loads in the network and under different criteria of making cell-edge decision. A hybrid model combining the analytical and simulation approaches has been developed to evaluate the average system throughput. It has been found that the results of the hybrid model are in line with the simulation based results. The benefit of the model is that the throughput of any possible call state in the system can be evaluated. Two empirical path loss models in an indoor-to-outdoor environment of a residential area have been developed based on the measurement data at carrier frequencies 900 MHz and 2 GHz. The models can be used as analytical expressions to estimate the level of interference by a femtocell to a macrocell user in link-level simulations.

Reinforcement Learning (RL) has in recent years become a core method for sequential decision making in complex dynamical systems, being of great interest to support improvements in scheduling problems. This could prove important to areas in the newer generation of cellular networks. One such area is the base stations scheduler which allocates radio resources to users. This is posed as large-scale optmization problem which needs to be solved in millisecond intervals, while at the same time accounting for multiple, sometimes conflicting, objectives like latency or Quality of Service requirements. In this thesis, multi-objective RL (MORL) solutions are proposed and evaluated in order to identify desired features for novel applications to the scheduling problem. The posed solution classes were tested in common MORL benchmark environments such as Deep Sea Treasure for efficient and informative evaluation of features. It was ultimately tested in environments to solve combinatorial optmization and scheduling problems. The results indicate that outer-loop multi-policy solutions are able to produce models that comply with desired features for scheduling. A multi-policy multi-objective deep Q-network was implemented and showed it can produce an adaptive-at-run-time discrete model, based on an outer-loop approach that calls a single-policy algorithm. The presented approach does not increase in complexity when adding objectives but generally requires larger sampling quantities for convergence. Differing scalarization techniques of the reward was tested, indicating effect on variance that could effect performance in certain environment characteristics. / Försärkningsinlärning som en gångbar metod för sekventiellt beslutsfattande i komplexa dynamiska system har ökat under de senaste åren tack vare förbättrade hårdvaru möjligheter. Intressenter av denna teknik finns bland annat inom telekom-indistrin vars aktörer har som mål att uteveckla nya generationens mobilnätverk. En av de grundläggande funktionerna i en basstation är scheduleraren vars uppgift är att allokera radio resurser till användare i nätverket. Detta ställs med fördel upp som ett optimeringsproblem som nödvändiggör att problemet måste lösas på millisekund nivå samtidigt som den kan ta flera typer av mål i beaktning, såsom QoS krav och latens. I detta examensarbete så presenteras och utvärderas förstärningsinlärnings algoritmer för flera mål inom flera lösningsklasser i syfte att identifiera önskvärda funktioner för nya tillämpningar inom radio resurs schemaläggning. De presenterade lösningsklasserna av algoritmer testades i vanligt förekommande riktmärkesmiljöer för denna typ av teknik såsom Deep Sea Treasure för att på effektivt sätt utvärdera de kvalitéer och funktioner varje algoritm har. Slutligen testades lösningen i miljöer inom kombinatorisk optimering och schemaläggning. Resultaten indikerar att fler-policy lösningar har kapaciteten att producera modeller som ligger inom de krav problemet kräver. Fler-policy modeller baserade på djupa Q-närverk av flera mål kunde framställa adaptiva, diskreta realtidsmodeller. Denna lösning ökar inte komplexiteten när fler mål läggs till men har generellt behov av större mängder samplade preferenser för att konvergera. Olika skaläriseringstekniker av belöningen testades och indikerade att dessa påverkade variansen, vilket i vissa typer av miljö konfigurationer påverkade resultaten.