Twin Delayed DDPG based Dynamic Power Allocation for Mobility in IoRT

The internet of robotic things (IoRT) is a modern as well as fast-evolving technology employed in abundant socio-economical aspects which connect user equipment (UE) for communication and data transfer among each other. For ensuring the quality of service (QoS) in IoRT applications, radio resources, for example, transmitting power allocation (PA), interference management, throughput maximization etc., should be efficiently employed and allocated among UE. Traditionally, resource allocation has been formulated using optimization problems, which are then solved using mathematical computer techniques. However, those optimization problems are generally nonconvex as well as nondeterministic polynomial-time hardness (NP-hard). In this paper, one of the most crucial challenges in radio resource management is the emitting power of an antenna called PA, considering that the interfering multiple access channel (IMAC) has been considered. In addition, UE has a natural movement behavior that directly impacts the channel condition between remote radio head (RRH) and UE. Additionally, we have considered two well-known UE mobility models i) random walk and ii) modified Gauss-Markov (GM). As a result, the simulation environment is more realistic and complex. A data-driven as well as model-free continuous action based deep reinforcement learning algorithm called twin delayed deep deterministic policy gradient (TD3) has been proposed that is the combination of policy gradient, actor-critics, as well as double deep Q-learning (DDQL). It optimizes the PA for i) stationary UE, ii) the UE movements according to random walk model, and ii) the UE movement based on the modified GM model. Simulation results show that the proposed TD3 method outperforms model-based techniques like weighted MMSE (WMMSE) and fractional programming (FP) as well as model-free algorithms, for example, deep Q network (DQN) and DDPG in terms of average sum-rate performance

Resource Allocation in Drone-Assisted Emergency Communication Systems

Due to low cost and high mobility, drones are considered important in emergency communications. In this thesis, we consider a unique drone assisted emergency communication system used in disaster scenarios, where the drone with limited power acts as a relay to improve the downlink sum rate through rational resource allocation. The wireless channel model between drones and ground users in emergency communications is different from conventional relay networks, while drones have their coverage area and data rate limits. Considering these specific characteristics, we formulate a joint power and subcarrier allocation problem to maximize data rate of users, which is limited by the transmit power budget per drone and the number of users on each subcarrier in emergency communications. However, resource allocation in a unique drone assisted emergency communication system is a nondeterministic polynomial time (NP)-hard problem requiring brute force search, which has prohibitive computational complexity. Instead, efficient algorithms that provide a good trade-off between system performance and implementation practicality are needed. The contributions of this thesis are proposing two different resource allocation schemes. Both schemes divide users into high-priority(HP) users and low-priority(LP) users and both guarantee minimum guaranteed rate for HP users. The first scheme is an adaptive algorithm with low complexity. In this scheme, a suboptimal solution is proposed by dividing users into two priority groups: HP users (rescuers) and LP users (affected people). This procedure achieves quasi-linear complexity in terms of the number of users. Finally, the data of the brute force search method and this method were collected through simulation experiments. The data shows that the data rate of the proposed scheme was very close to the optimal data rate when there was a lack of resources. The second scheme is an adaptive algorithm. In the proposed scheme, we formulate a joint power and subcarrier allocation problem to maximize data rate of users, which is limited by the transmit power budget per drone and the number of users on each subcarrier in emergency communications. Due to the intractability of the formulated problem, it is decomposed into two sub-problems: power allocation optimisation and subcarrier allocation optimization. Then a joint resource allocation algorithm is proposed. The simulation results show that the performance of the proposed method is close to that of the optimal solution but with much lower complexity